State of the Art and Recent Advancements in the GIS-Based Approaches for Landfill Site Selection

Abstract

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling.

1. Introduction

Achieving sustainable growth is fundamentally dependent on the responsible stewardship of natural resources and the mitigation of environmental pressures. Consequently, the improper management of municipal solid waste (MSW) generated by human activity remains a primary factor contributing to the global increase in pollution, particularly in developing economies [1]. As stated in [2], the solid waste produced by commercial, industrial, institutional, and residential activities is referred to as municipal solid waste, and it differs greatly between countries due to socioeconomic and cultural variables. Population growth, industrial activity, urbanisation, dietary changes, and growing living standards are all closely associated with the production of municipal solid waste. Additionally, waste production rises as more people move from rural to urban regions because of increased consumption. The generation of solid waste from cities is predicted to significantly increase, with global volumes predicted to reach 3.86 billion tonnes by 2050. This is a 50% increase from 2022 levels, primarily because of the quick changes in the economies and demographics of developing nations. Waste creation is expected to double or more in low-income nations, especially in South Asia and Sub-Saharan Africa [3]. Despite global efforts toward circularity, landfilling and open dumping remain the predominant methods of waste disposal, particularly in lower-income economies. On a global scale, approximately 30% of all generated waste is currently either openly dumped or left uncollected. In areas characterised by technological constraints and inadequate infrastructure, illegal dumping remains prevalent, with waste often deposited in environmentally sensitive locations, including riverbanks and open spaces, posing significant ecological risks [4,5,6]. Although environmental protection has become a central tenet of international policy, the prioritisation of such strategies is inconsistent across different socioeconomic contexts [7,8]. These areas often lack the institutional capacity to implement modern policy frameworks, such as the ‘polluter pays’ principle or the ‘reduce, reuse, recycle’ hierarchy, thereby exacerbating the long-term ecological and public health risks associated with unmanaged waste.

Municipal solid waste management is critical to the population’s health and well-being as well as the long-term sustainable growth of cities [9]. However, this is still a major environmental issue, and underdeveloped countries struggle since many of their citizens still dispose of their waste improperly in landfills and open dumping [10,11,12]. Many cities face widespread, indiscriminate dumping as a result of poor governance, a lack of land-use planning, and the world’s growing urbanisation [13,14,15]. Selecting a landfill location is a crucial step in the solid waste disposal planning process. This procedure is essential for reducing the negative effects on the environment and guaranteeing long-term operating safety [16,17]. Negative externalities are the detrimental effects that inappropriate landfill placement has on the environment, society, and public health, which include groundwater and surface water contamination from leachate migration, soil degradation, air pollution from landfill gas emissions, offensive odours, visual effects, biodiversity loss, public health risks, and social conflicts that affect nearby populations [18,19,20].

Furthermore, it has detrimental effects, such as geological, social, and environmental problems with both immediate and long-lasting impacts on society [6,21]. Residents frequently oppose the establishment of landfills due to their associated negative environmental impacts [4]. Research has shown that communities that generally oppose landfill construction for a range of environmental reasons (such as soil, water, and air pollution) encounter pollution difficulties [22]. Therefore, it is essential to carefully select landfill locations to handle these issues [23]. In order to minimise local contamination and civil instability, landfills must be located far from waste sources. They must also follow requirements for site design, operations, maintenance, and post-closure monitoring [24,25,26]. Landfill site selection is a challenging spatial decision-making process that involves numerous conflicting criteria. In addition to adhering to all standards and limiting impacts on human and ecological health, the site must take environmental, social, and economic factors into account [27,28,29]. Likewise, refs. [30,31] found that one of the most important problems in SWM worldwide is selecting the optimum disposal locations. To solve this problem, GIS (Geographic Information Systems) and MCDA (Multi-Criteria Decision Analysis) methods, such as the AHP, are commonly used [32]. According to a report in [33], GIS can be used to organise, manage, and visualise geographic data. By providing a framework for organising, presenting, and integrating different factors to obtain a decision, these techniques simplify challenging decision-making. Additionally, they enhance landfill selection by using techniques that compare choices with predetermined criteria [34,35,36,37]. In order to address this, combining them makes it possible to assign weights and scores to every possible option, which makes decision-making easier [12,19,38,39,40,41,42].

Despite the fact that choosing a landfill location is primarily an administrative choice, experts argue that a number of frameworks and strategies have improved this process. This is because landfill location requires a careful evaluation of many variables, including economic constraints, environmental preservation, and public health [43]. According to earlier studies, the process consists of two main steps: (1) identifying potential sites through preliminary screening, and (2) evaluating their suitability by considering financial, engineering, economic, and environmental issues. In contrast, other studies suggested a four-phase strategy: (1) locating potential sites using exclusion criteria; (2) ranking potential sites; (3) evaluating site suitability; and (4) selecting the final location [27,28,44,45,46]. While there has been prior research on GIS and MCDA methods for landfill site selection, the majority of this work is unorganised and lacks precise selection criteria, data sources, weighting strategies, and decision-making methodologies. Administrative records, GPS surveys, previously published materials, reports, and instructions are some of these data sources. High-resolution datasets from UAS-PPK are frequently used in urban settings in contemporary surveying methods to overcome the mapping difficulties caused by densely populated regions [47].

A significant number of earlier reviews have summarised the available research on landfill site selection, but they are limited in including a thorough assessment of the various approaches’ usage status, their advantages, drawbacks, and limits, as well as recommendations for further study. Therefore, this work aims to overcome this gap by carefully and critically evaluating GIS-based MCDA for landfill site selection. The methodological evaluation’s primary subjects include the choice of landfill-siting criteria, GIS data and information sources, exclusion restrictions, criteria-weighting techniques, MCDA models, validation procedures, and sensitivity and uncertainty analyses. It also examines the geographic distribution and temporal evolution of published articles to identify important research trends and support methodological developments. A comprehensive search, screening, eligibility assessment, and qualitative synthesis were conducted for relevant articles published between 2016 and 2026. The advantages, drawbacks, and applicability of various methodologies are compared, and the integration of validation, sensitivity, and uncertainty analyses into landfill-siting decision-making is examined. The review finds methodological flaws and limitations in existing procedures throughout this overall assessment. In order to create more transparent, trustworthy, and sustainable landfill-site-selection frameworks, it suggests future research goals and methodological frameworks. The thorough process for identifying appropriate municipal solid waste dump locations utilising GIS and multi-criteria is shown in Figure 1.

In general, the reviewed literature suggests that a well-designed GIS-MCDA framework must consider environmental, social, economic, and technical issues when choosing landfill locations. Therefore, it is necessary to understand the principles, advantages, and developments of these concepts to improve the reliability of landfill placement. For this reason, the section below outlines the systematic review technique used to analyse the relevant studies.

2. Methodology

The review was conducted through a detailed systematic investigation of literature search databases. Additionally, terms, keywords, and phrases were used to identify relevant publications in databases such as Scopus, Web of Science, ScienceDirect, Google Scholar, SpringerLink, and IEEE Xplore. The search was performed using terms such as Criteria, spatial analysis and planning, suitability maps, environmental criteria, MCDA, AHP, land suitability assessment, and fuzzy methods. Other terms include Geographic Information System, Analytic Hierarchy Process, Multi-Criteria Decision Analysis, Remote Sensing, Fuzzy Set Theory Analysis, Boolean Method, Weighted Linear Combination, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Analytic Network Process (ANP), landfill, soil pollution, and landfill suitability analysis. With these terms, the search was conducted on recently published works with content related to the previously mentioned domains. The initial search identified approximately 195 publications (Figure 2). After reviewing the title, abstract, and full documents, any duplicate records, editors’ papers, theses or dissertations, predated studies, and those lacking enough methodological detail to be relevant to landfill site selection were excluded from consideration. Finally, the only studies included were those which (i) were published in peer-reviewed journals, (ii) had relevance to site selection and suitability assessment of landfills, (iii) applied methods like GIS, remote sensing, MCDA, optimisation, or different hybrid approaches in decision-making, (iv) contained adequate methodological details, and (v) were published in English between the years of 2016 and 2026. As a result, 175 articles were included.

The quality of the methods used in the selected studies was analysed, and useful data on study features, GIS and MCDA methods, assessment criteria, weighting methods, validation methods, and key results were gathered and organised into thematic categories and tabular forms with respect to the major statistical data needed from the articles to identify trends in the methodology and identify gaps in the research conducted. The studies analysed could come from any part of the world, as this was not limited.

The selected studies were systematically organised and categorised to facilitate a comprehensive analysis of the reviewed literature. Bibliometric data were taken from each selected article, including publication year, journal source, country of study, and methodological features; weighting methods, MCDA methods, validation process, uncertainty and sensitivity assessment were extracted from each article. The data that were taken from the articles were organised thematically and then synthesised in such a way that allowed for the comparison of methods used, noting trends in research, determination of the frequency of usage of the methods, as well as identification of research gaps and areas for further research. The findings are presented in accordance with the thematic categories set out in the Results.

3. Results

This systematic review is presented in this section through sequential analysis of the selected publications. A comprehensive evaluation of the methodological aspects of landfill site selection is presented, including siting criteria, suitability threshold values, GIS data sources, criteria weighting techniques, MCDA models, model validation, and sensitivity and uncertainty analyses, as well as publication trends, journal distribution, and geographical distribution. A thorough overview of recent research advancements, methodological constraints, and potential future study directions in landfill site selection based on GIS and MCDA is provided.

3.1. Annual Publication Trends of Review Studies

Based on the review’s analysis, it is mentioned that the use of GIS, RS, and MCDA techniques in the field of sustainable waste management using landfills has been continuously expanding. Since 2016, the yearly publication activity has grown dramatically, peaking in 2025. Because publications released prior to the date of the literature review are taken into account, there are fewer research articles reported in 2026. The increasing trend consequently validates the increasing importance of spatial decision-making methods in the selection of landfill locations. The distribution of research articles from 2016 to 2026 is illustrated in Figure 3.

Over the duration of the study period (2016–2026), research using GIS- and MCDA-based approaches for landfill site selection has expanded gradually, reflecting the growing acceptance of spatial decision support for addressing the planning and environmental difficulties involved with landfill location. Even if there was a slight decline in publications in 2024, this fluctuation is most likely due to year-to-year variations in the dataset being studied rather than a major change in the study’s overall direction. The continued rise to 28 publications in 2025 indicates that interest in using GIS and MCDA for landfill site selection is still growing.

3.2. Journal Distribution of Reviewed Studies

The study on landfill site selection that has been published in numerous peer-reviewed journals demonstrates the discipline’s interdisciplinary nature. The largest number of studies were from Environmental Earth Sciences, Sustainability, Environmental Monitoring and Assessment, Environmental Science and Pollution Research, followed by International Journal of Environmental Science and Technology and GeoJournal. The remaining studies were distributed among different environmental, engineering, and geological journals, indicating the growing interest in GIS, remote sensing, and MCDA techniques for sustainable landfill site selection. The top 20 journal distribution and summary articles utilised in this review are displayed in Figure 4. This shows how research on landfill site selection based on GIS and MCDA is distributed throughout a wide range of peer-reviewed publications. The multidisciplinary aspect of landfill site selection, which incorporates environmental management, GIS technology, and multi-criteria decision-support systems, is highlighted by this distribution. A second indication of the wide disciplinary application and increasing interest in GIS- and MCDA-based approaches to landfill site selection is the existence of studies published in many journals.

3.3. Geographic Distribution of the Reviewed Studies

In order to handle solid waste sustainably worldwide, landfill site selection and optimisation are essential components [48]. A total of 175 studies, 164 original research articles and 11 review articles, were analysed in this systematic review. Between countries and regions, the distribution of studies varied considerably. Based on this analysis, India, Iran, Iraq, and Turkey are represented in the majority of the studies, which indicates that these countries have a particularly strong research focus on landfill selection and optimisation using GIS and MCDA. But in developed countries, there are limited studies. The table below lists the 30 nations along with the percentage share and number of studies.

Table 1. Geographic distribution of the reviewed landfill site selection studies by country (2016–2026, n = 175).

Rank	Country	Study	% (n = 175)	Rank	Country	Study	% (n = 175)
1	India	30	17.14	15	Egypt	3	1.71
2	Iran	26	14.86	15	Greece	3	1.71
3	Iraq	13	7.43	15	Indonesia	3	1.71
3	Turkey	13	7.43	15	Italy	3	1.71
5	Morocco	10	5.71	20	Kuwait	2	1.14
6	Ethiopia	7	4.00	20	Libya	2	1.14
6	Nigeria	7	4.00	20	Palestine	2	1.14
8	Vietnam	6	3.43	20	Peru	2	1.14
9	Algeria	4	2.29	20	Serbia	2	1.14
9	Ghana	4	2.29	26	Brazil	1	0.57
9	Malaysia	4	2.29	26	China	1	0.57
9	Pakistan	4	2.29	26	Ecuador	1	0.57
9	Saudi Arabia	4	2.29	26	Lithuania	1	0.57
15	Bangladesh	3	1.71	26	Philippines	1	0.57

illustrates an unequal distribution of research on waste site selection among countries. India (17.14%) and Iran (14.86%) lead the literature, followed by Iraq (7.43%), Turkey (7.43%), and Morocco (5.71%). Ethiopia and Nigeria contributed 4% of the studies, whereas most other countries contributed less than 3%. Overall, the findings show that research is concentrated on developing countries, where growing concerns about solid waste management and increased urbanisation have prompted the use of GIS-based and multi-criteria decision-making techniques [23].

3.4. Landfill Siting Criteria and Exclusion Constraints

Since landfilling remains the primary method of waste disposal in many places, selecting a specific site location is crucial [43]. To help decision-makers manage the challenge of site selection, researchers provide reliable techniques and evaluation frameworks. To ensure long-term sustainability, these research contributions provide the standards needed to balance economic, social, and environmental factors [5,19,49,50]. The literature shows that because siting requirements vary worldwide, different selection criteria are used to match local conditions. Consequently, the initial phase of the suitability analysis typically comprises identifying specific attributes that align with the research objectives and the specific geographic context [51,52]. Since landfills are used to dispose of waste and improper management can have negative consequences for the environment and public health, this framework needs to consider a number of factors [30]. These factors are often developed from the existing literature, accessible datasets, and the unique features of the region under consideration in the majority of suitability evaluations [53]. Researchers enable regulatory bodies to identify sites that limit negative effects by establishing scientific benchmarks. In this framework, it is widely acknowledged that in order to protect human health and ecosystem integrity, disposal facilities should be situated a considerable distance from populated regions, public spaces, and environmentally sensitive zones [37,54].

A comprehensive evaluation of risks to the environment and public health should be the foundation for selecting a location for a new MSW landfill, along with adherence to any applicable local regulatory requirements. A wide range of variables are taken into account in site suitability evaluations. For instance, ref. [21] outlined a wide range of evaluation criteria, including distance from airports and populated areas, geology, permeability, slope, soil type, aspect, elevation, roads, coastline, lakes, rivers, streams, distance from lineaments, distance from protected areas, wind speed, profile curvature, land value, land use/land cover, groundwater, susceptibility to landslides, and railroads. These standards are methodically categorised as either restrictions or elements to minimise negative socio-environmental effects and balance construction expenses [52,53,55]. Certain distance ranges are usually used in standard site selection procedures; these limits are calibrated based on the intrinsic sensitivity of each evaluated area [56,57,58].

Since selecting a suitable disposal site is challenging and time-consuming, accurate geographical data is crucial. The effectiveness of the suggested action would ultimately be undermined by inaccurate data, leading to incorrect analysis [59]. Solving these issues is necessary for accurate GIS-based landfill siting outcomes. Multi-criteria approaches based on GIS are helpful tools for handling difficult decision-making situations. Professionals are advised to concentrate on the most essential criterion, even when several elements exist. The majority of scholars think that since improper landfill placement can have detrimental effects on the environment and public health, all relevant aspects must be taken into account. Additionally, because certain locations, such as airports and military bases, are inappropriate due to safety concerns, restrictions are crucial to the siting process [60,61]. The most often reported parameters for MSW disposal site selection were determined by a thorough review of the literature.

3.4.1. Environmental Criteria

Environmental factors are often the primary factors considered when choosing a landfill site [19]. Current land use is a fundamental component of almost every recommended landfill selection procedure. Disposal sites must be carefully placed at a considerable distance from forests and hydrological features to prevent adverse effects on wildlife, aquatic ecosystems, and public health. To ensure long-term operational viability, potential future expansion should be considered while selecting a location. Land-use/land-cover (LULC) categories like vegetation, agricultural land, outcrops, and urbanised areas are crucial markers of suitability in this setting [62,63]. Generally, barren land, sparsely vegetated areas, and land with low agricultural productivity are considered the most suitable options for landfill development.

When selecting a landfill, preventing water pollution is also essential. Sites should be located far from bodies of surface water, such as rivers, lakes, marshes, and seas, to reduce the risk of pollution and runoff. Low soil permeability and appropriate aquifer conditions significantly decrease groundwater contamination. Because landfills near rivers, lakes, streams, or ponds increase the risk of leachate pollution, buffer zones must be established to stop infiltration and protect surrounding ecosystems [64,65,66,67,68]. Maintaining a safe distance from drainage networks is also essential for controlling runoff and the spread of pollutants, particularly in areas with high precipitation. It is generally advised to maintain a distance of at least 500 m from surface water. To identify any leachate infiltration and guarantee landfill integrity, groundwater depth and quality must be continuously monitored. To safeguard public health, landfills should also be located at a safe distance from drinking water wells.

The code for sanitary landfilling of domestic waste should be adhered to when assessing the impact of landfills on neighbouring communities and the environment, to mitigate issues such as noise, vermin, fire hazards, air and water contamination. These sites should be located at a distance from both urban and rural areas [69]. Inadequate solid waste management may pose a major threat to residents of nearby areas, and it can negatively affect the neighbourhood. This is because of their unpleasant odours, noise, and increased risk of disease, particularly during the wet season. As a result, landfills must be located far enough from populated areas [9,64,70].

3.4.2. Socio-Economic Criteria

In urban centres, built-up areas comprise not only residential areas but also government buildings, schools, hotels, markets, hospitals, and other establishments. Disease vectors like rats, flies, mosquitoes, and other creatures that can spread illness to people are able to flourish in landfill environments. Hazardous gases, leachate, and smells are all produced by solid waste and pose serious health problems to people [71]. Furthermore, these places offer suitable conditions for viruses that might infect people. A buffer zone should encompass built-up areas to address these issues and ensure sustainable urban expansion [70,72,73].

Locating landfills within a 1000 m buffer zone from road networks is generally advised. However, it should not be situated too far from roads because site suitability decreases with distance due to higher transportation costs [74,75]. Though being near streets lowers travel costs, there can sometimes be aesthetic problems [62]. According to [63], the proximity of landfills to transportation networks significantly influences their selection. Locations near roads usually receive better scores since they are easier to get to. A moderate distance, neither too far nor too close, was preferable [60,70,72,76]. Placing landfills far from train tracks improves compatibility by reducing noise, safety hazards, and potential environmental impacts on the community [9]. It should be kept at least 500 m from a train due to its unattractive appearance and expected odour [77,78].

Landfills should be located a safe distance from airport facilities because they attract birds, increasing the risk of bird–aircraft collisions, and also pose other risks, such as smoke from fires that can obstruct air traffic [31,76]. Similarly, ref. [39] noted that aviation safety may be exposed to bird flocks associated with landfills. Thus, it is crucial to keep a sufficient distance between landfills and airports [69].

Waste disposal sites should be located at a safe distance from monuments, historic structures, government buildings, temples, parks, and other locations that could be endangered by harmful gases and leachate from solid waste [63]. Park areas set aside for vegetation conservation, restoration, and rehabilitation should also be taken into account [68]. The research states that landfills cannot be built on national parks or historical sites. Landfills should be at least 1000 m away from these sites. This criterion gives a score of 0 for distances under 1000 m, whereas suitability increases with distance and approaches a value of 1, which indicates maximum suitability [79].

Decision-makers must carefully consider site acquisition costs as a key economic factor, since building new landfill infrastructure is a significant financial burden. The availability of technically optimal locations may be restricted by high land prices, which often serve as a major constraint. The selection process is made more difficult by the fact that land values are fundamentally linked to an area’s accessibility to important transportation connections and local population density. It is generally advised that land set aside for such facilities be economically feasible in order to maximise capital expenditures [69]. The accuracy of the site selection process is greatly increased when land value is incorporated into suitability evaluations [80]. As a result, areas with lower land values, often located far from urban centres, are usually the best sites for sanitary landfills [35].

3.4.3. Geological, Geomorphological and Topographic Criteria

Studies show that selecting suitable landfill areas can be significantly complicated by the existence of geological fractures, such as faults and joints. Leachate migration can contaminate surface and groundwater through these fractures. Thus, it is essential to stay away from places with high structural lineament density while choosing landfill locations [59]. To further lower the potential for environmental harm, landfills should be situated at a safe distance from significant fault lines because unstable and porous zones increase the risk of leachate contaminating groundwater [65,68]. Moreover, the study in [81] shows how crucial it is to prevent problems while selecting a location for a disposal facility. Locations within 500 m of a fault are considered undesirable (given a value of 0), while those beyond 1000 m are considered suitable (assigned a value of 1).

Lineament density was one of the factors limiting landfill sites. Areas with high lineament density are not the best for landfills (disposal sites) due to increased groundwater contamination, as these are areas of localised weathering with high permeability and porosity, where groundwater usually flows [62]. Furthermore, lineaments, as defined in [9], are linear formations on the surface of the Earth that frequently show faults affecting the permeability and porosity of rocks. Some of these fractures may come into contact with groundwater, raising the possibility of contamination. To prevent contaminating groundwater, landfills should be situated far from regions with high lineament density [76].

Slope directly affects drainage and site stability [82] and is therefore a key parameter that must be integrated into the evaluative criteria for MSW landfill siting. Increased runoff from steeper slopes can transport waste and debris into lower-lying areas, endangering human health. The acceptability of sites declines with increasing slope steepness [62]. Also, [68] confirms that the site’s slope, which greatly enhances surface runoff, can carry pollutants from landfills into groundwater and surface water. According to [9], because ground slope influences runoff, erosion, and trash dispersion, all of which have an impact on landfill performance, gentle slopes are frequently chosen over steep ones because they help restrict waste and lower environmental risk. Additionally, steep slopes can raise the expense of excavation and landfill development. Thus, from an economic perspective, locations with steep slopes have significantly greater construction costs than those with moderate slopes [70]. It is believed that steeper slopes and higher elevations present greater environmental risks, rendering them unsuitable for disposal sites. Generally, level terrain or lower altitudes are preferred because they lower the hazards of leachate migration and pollution [9,66].

Soil permeability, geochemical characteristics, and the integrity and depth of soil layers are important factors. Particularly in metropolitan locations, it plays a crucial role in evaluating the suitability of landfills. Deeper, well-organised layers and low-porosity soils serve as organic barriers that restrict leachate migration into groundwater [78,83]. Because low recharge rates lower the risk of groundwater pollution, landfills work best in regions with impermeable soils like clay and silty clay [21,28,68,84]. Low-permeability soil plays a critical role in landfill design by reducing the risk of groundwater contamination, particularly when liners do not fully prevent leachate leakage. It is also typically defined by a hydraulic conductivity (kf) ≤ 1 × 10⁻⁹ m/s. Among various soil types, sand exhibits the highest permeability, clay the lowest, and other soils fall within this range [85]. Sandy loam soils, due to higher porosity, are less suitable for landfill siting than silty clays, which have lower porosity and better containment properties [81]. While subsurface stoniness can have a detrimental impact on landfill design and stability, high-porosity soils raise the risk of leachate migration and contamination. On the other hand, low-permeability soils protect groundwater effectively and are typically better suited for landfill construction. Furthermore, deeper soil improves economic viability by lowering operating costs and increasing containment capacity [86,87]. Therefore, to guarantee both structural integrity and environmental protection, landfill sites should be situated in regions with stable, low-permeability soils [85,88].

The permeability and porosity of the bedrock must be considered while selecting a landfill location in order to reduce the possibility of groundwater contamination. Several investigations give particular rock types priority when deciding where landfills should be placed [64,77]. This emphasises how crucial it is to stay away from fault zones in order to reduce environmental issues [74]. According to [65], the selection of landfill location is significantly influenced by the underlying geology, which includes limestone, basalt, and alluvial deposits. The permeabilities of particular rock types play a major role in regulating the migration of leachate from landfills. Geomorphology, the science of changes in land features, has a significant influence on landfill site selection; for instance, the study in [89] classified the study area as highly dissected, moderately dissected, or undissected. Undissected areas are considered the most suitable for waste disposal, while extensively dissected areas are considered undesirable. Studies indicate that areas with simple lithology allow more accurate predictions of pollutant dispersion, as lithology and layering play a key role in leachate migration [59,81,90].

The frequency and distribution of criteria used in landfill site selection were determined by analysing the 175 identified articles. This included all the examined literature that reported, specified, or addressed landfill siting criteria. The findings showed that the criteria considered in the examined research varied significantly, and 36 of the most often used criteria were identified based on how frequently they occurred. The most commonly reported criteria in site-suitability assessments were slope (84.6%), surface water and groundwater-related parameters (91.4%), and road and transit accessibility (93.7%).

Table 2. Ranking of the most frequently used landfill site selection criteria based on 175 reviewed studies.

Rank	Criterion	Articles	(%)	Rank	Criterion	Articles	(%)
1	Roads/Transportation	164	93.7	19	Aspect	26	14.9
2	Surface Water & Groundwater	160	91.4	20	Vegetation/NDVI	24	13.7
3	Slope	148	84.6	21	Power Lines	22	12.6
4	Residential Areas	138	78.9	22	Industrial Areas	20	11.4
5	(LULC)	131	74.9	23	Climate/Temperature	19	10.9
6	Soil	91	52.0	24	Administrative Boundaries	17	9.7
7	Elevation	81	46.3	25	Wind Direction	17	9.7
8	Geology	77	44.0	26	Archaeological	17	9.7
9	Protected Areas/Forests	65	37.1	27	Land Value/Economic Cost	15	8.6
10	Airports	60	34.3	28	Utility Infrastructure	14	8.0
11	Fault Lines/Seismic Zones	54	30.9	29	Parks/Recreation Areas	12	6.9
12	Railways	48	27.4	30	Bedrock Depth/Geomorphology	11	6.3
13	Population Density	44	25.1	31	Soil Permeability	11	6.3
14	Rainfall/Precipitation	41	23.4	32	Landfill Buffer Zones	9	5.1
15	Groundwater Depth	36	20.6	33	Economic Development Zones	8	4.6
16	Lineament Density	33	18.9	34	Tourism/Scenic Areas	8	4.6
17	Lithology	30	17.1	35	Soil Texture	6	3.4
18	Flood Risk/Floodplain	28	16.0	36	Protected Wetlands	5	2.9

lists the 36 most commonly found criteria along with the related occurrence frequencies.

According to the data, of the 175 investigations, 164 included road and transit as criteria, 160 included surface water and groundwater, and 148 included slopes. The disparate frequencies show that different landfill site selection studies do not use the same set of parameters. Rather, the criteria were chosen in accordance with local environmental, social, regulatory, and methodological requirements. Additionally, several land-cover categories occurring less frequently in the reviewed studies were identified as potentially suitable for landfill development, including bare land [26,53,90,91], sparsely vegetated areas [35,86], wasteland [92], areas with low agricultural value [84,93], and arid land [94].

3.5. Suitability Threshold Values for Landfill Suitability Selection

Researchers point out significant agreement on the key criteria for selecting a waste disposal site. Environmental aspects of site selection, such as distance from rivers, groundwater, settlements, land use, and slope, were most popular because they have a direct impact on both the environment and public health [95]. Geographical and hydrological site selection factors, such as soil type, lithology, and groundwater susceptibility, were extensively used to reduce leaching risk [90]. As seen in

Table 3. Commonly reported unsuitable and highly suitable thresholds for landfill site selection criteria identified based on 164 reviewed studies.

Criterion	Unsuitable/Low Suitable Threshold	Highly Suitable Threshold	Justification	References
Road distance	<300 m	1000–3000 m	Minimises nuisance while maintaining economical access to transportation.	[39,54,55,96,97,98] [21,31,55,84,97] [31,39,61,81,97] [29,31,39,54,83,99] [10,18,81,96,100] [17,39,50,80,96,101] [39,54,83,98] [9,17,67,98,101,102] [17,97,103,104,105] [39,72,97,99,106] [75,89,101,107] [14,64,90,108,109] [14,61,106,108] [29,55,61,72] [35,103,106,110] [39,83,98,111,112] [95,96,113,114]
Slope	>15°	0–5°	Gentle slopes improve landfill stability and reduce erosion risk.
Settlement distance	<1000 m	>2000 m	Reduces odour, noise, visual impacts, and public health concerns.
River/Stream distance	<300–500 m	>1000–2000 m	Protects surface water from leachate contamination.
Surface water	<500 m	>1500–2500 m	Minimises contamination of lakes and reservoirs.
Groundwater wells	<300–400 m	>1500 m	Protects groundwater quality and drinking-water sources.
Airport distance	<3000 m	>5000 m	Minimises bird-strike hazards.
Elevation	<500 m or >2500 m	500–2000 m	Moderate elevations generally provide better accessibility and drainage
Fault distance	< 500 m	>1000–5000 m	Avoids tectonically unstable zones and fracture-controlled leachate migration.
Protected areas	Within protected area	>1000 m buffer	Conserves biodiversity and satisfies environmental regulations.
Land use/Land cover	Urban land, wetlands, forests	Bare land, shrubland, degraded land	Minimises land-use conflicts and ecological impacts.
Geology	Highly permeable formations	Clay-rich or low-permeability formations	Restricts downward leachate migration.
Soil type	Sandy soils	Clay/clay-loam soils	Clay provides a natural barrier against contaminant migration.
Forest distance	Inside forest	>500–1000 m	Protects forest ecosystems and biodiversity.
Wetlands	Inside wetland or <1000 m	>3000 m	Preserves ecologically sensitive wetland environments.
Railway distance	<300 m	>300–1000 m	Reduces operational hazards and transportation conflicts.
Drainage density	>3 km/km2	<1 km/km2	Lower drainage density reduces contaminant transport pathways.
Flow accumulation	High	Low	Low flow accumulation minimises runoff concentration and flooding potential.	[11,115] [11,53] [73,77,113] [69,116,117,118] [49,67,119] [13,54,67,112] [29,97,99,120] [68,97,99,105] [121,122] [56,89,97]
(TWI)	>10	<6	Low TWI indicates drier conditions with lower leachate migration potential.
Lineament density	>2 km/km2	<1 km/km2	Low fracture density reduces groundwater contamination risk.
Population density	>1000 persons/km2	<250 persons/km2	Reduces exposure of nearby communities.
Cultural/heritage sites	<1000 m	>2000 m	Protects archaeological and cultural heritage resources.
Industrial areas	<500 m	500–1500 m	Minimises land-use conflicts while maintaining accessibility.
Power lines	<100 m	>300 m	Ensures operational safety and infrastructure protection.
Coastline	<1000 m	>3000 m	Protects coastal ecosystems and marine water quality.
Wildlife habitat	Within habitat	>1000 m	Minimises habitat disturbance and biodiversity loss.

, another important point is that there are no uniform buffer distances or exclusion criteria for determining where to locate landfill sites. Studies apply buffer distances around features that may be sensitive to landfill activities, e.g., rivers, settlements, roads, airports, and protected areas. However, the distance values recommended in different publications vary greatly. Variations in these parameters are driven by regional legislative regulations, environmental factors, and operational techniques [39,95].

Among 175 articles included in the review, 164 were original research articles, and 11 were review articles; The threshold-value analysis was based on the 164 research articles that reported these values and/or suitability classes for landfill site-selection criteria. As Table 3 illustrates, the threshold values used to determine suitable and unsuitable areas for landfill site selection differ greatly amongst the studies that were analysed. For relatively large exclusion distances, environmental protection standards are typically cited, particularly in relation to settlements, surface water, groundwater wells, airports, faults, protected areas, and wetlands. For example, highly suitable locations are usually located more than 2000 m from populous areas, 1000–2000 m from rivers and streams, more than 1500 m from groundwater wells, and more than 5000 m from airports. These restrictions primarily reflect the need to reduce risks associated with leachate contamination, public exposure, ecological disturbance, and operational dangers.

The most frequently used threshold values for each criterion are summarised in the table based on the literature review rather than the conventional threshold criteria.

3.6. GIS Data Sources

GIS data sources can provide the spatial information required to identify the best location for a landfill. The literature analysis indicates that studies utilised various data sources to support accurate landfill site selection and ensure data quality for GIS and MCDA. These data sources include Common datasets Sentinel, Landsat, Earth Explorer, United States Geological Survey (USGS), Shuttle Radar Topography Mission (SRTM), ASTER, European Space Agency (ESA) data, OpenStreetMap (OSM), NASA Earth observations, DIVAGIS, Geological Survey of India (GSI), the Harmonised World Soil Database (HWSD), World Clim, Google Earth Pro, and datasets from the Food and Agriculture Organisation (FAO), Africa Groundwater Atlas (AGWA), and the Society for Geoinformatics and Sustainable Development (SGSD) [87,92,123,124,125,126,127].

Satellite data commonly derived from GIS datasets that were used in landfill suitability analysis include Elevation, Slope, Aspect, Curvature Topographic Wetness Index (TWI), Land Use/Land Cover (LULC), Temperature of Land Surface (LST), Normalised Difference Vegetation Index (NDVI), Geology, Lithology, Soil type, Soil texture, Soil permeability, Groundwater depth, Groundwater vulnerability, Rivers, Streams, Lakes, Drainage system, Rainfall, Temperature, Road system, Railroad network, Airports, Community (residential areas), Urban Areas, Population Density, Protected Areas, Wetland, Forest, Administrative Boundary, and Confirmed Landfill sites [37,38,65,116,128]. To enhance the precision of these atmospheric models, GNSS observations are widely used in water vapour determination. Furthermore, identifying the annual seasonal signal in a GNSS station’s time series is crucial to account for cyclical environmental displacements [129].

The GIS data-source analysis includes 164 research articles out of the 175 examined publications, while 11 review articles were left out. The most commonly utilised geospatial data sources were found to include DEMs, geological maps, road networks, and datasets linked to groundwater. Their extensive spatial coverage, availability, and accessibility through publicly accessible repositories like OpenStreetMap (OSM) and national and international geospatial data providers are the main reasons for their widespread use. Only a small number of researchers use high-resolution datasets like LiDAR, UAV imagery, or detailed hydrogeological data [130,131,132,133]. The preference for medium-resolution datasets might reduce the spatial accuracy of landfill suitability maps in urbanised areas or regions with specific environmental characteristics [16]. The frequency of GIS data sources in all of the examined studies is summarised in

Table 4. Frequency of GIS data sources used in landfill site suitability assessment, a systematic review of 164 studies.

No.	GIS Data Source	Studies	(%)	No.	GIS Data Source	Studies	(%)
1	DEM	140	85.4	21	Administrative Boundary	14	8.5
2	Geological Maps	98	59.8	22	Population Density	12	7.3
3	Road Network	70	42.7	23	Railway Network	10	6.1
4	Groundwater/Wells	61	37.2	24	Airport Locations	9	5.5
5	Soil Maps	60	36.6	25	Protected Area Map	9	5.5
6	LULC	54	32.9	26	Forest Cover	8	4.9
7	Landsat Imagery	50	30.5	27	Wetland Inventory	8	4.9
8	Rivers/Streams	45	27.4	28	NDVI	8	4.9
9	Surface Water Bodies	41	25.0	29	Geomorphology Map	7	4.3
10	Topographic Maps	33	20.1	30	ASTER DEM	6	3.7
11	Meteorological Data	29	17.7	31	Borehole Database	5	3.0
12	(OSM)	29	17.7	32	Climate Data	5	3.0
13	Protected Areas	26	15.9	33	LST	4	2.4
14	Settlement Data	20	12.2	34	Google Earth Imagery	4	2.4
15	Sentinel-2 Imagery	20	12.2	35	Geophysical Data	3	1.8
16	Hydrogeological Maps	18	11.0	36	UAV/Aerial Imagery	2	1.2
17	Fault Maps	17	10.4	37	SPOT Imagery	2	1.2
18	SRTM DEM	17	10.4	38	LiDAR Data	2	1.2
19	Digital Soil Database	15	9.1	39	WorldView Imagery	1	0.6
20	Drainage Network	15	9.1	40	QuickBird Imagery	1	0.6

.

3.7. Criteria Weighing Methods

A key component of GIS-based landfill site selection is giving evaluation criteria the proper weights because the final suitability assessment is directly impacted by the relative relevance of environmental, social, economic, and technological aspects [134]. By methodically measuring each criterion’s contribution, several criteria weighting techniques have been created to increase decision-making’s transparency and dependability. The AHP and FAHP are the most commonly used of these because of their capacity to enable complicated spatial decision-making, handle ambiguity, and include expert judgement. The following subsections provide an overview of the concepts, computational procedures, and uses of these weighting strategies in landfill site selection [135].

3.7.1. AHP and Hierarchical Methods

Saaty’s AHP, one of the most widely used MCDA techniques, offers a strong theoretical foundation for analysing complicated problems with several interrelated objectives [80]. It employs a pairwise comparison matrix to evaluate the relative importance of criteria and provide consistency in decision-making across sectors. This method works especially well when dealing with issues with competing goals and several options [136,137]. It also breaks down a decision problem into a hierarchy of smaller, easier-to-manage components that may be examined independently. The strategy promotes rational decision-making when there are conflicting objectives. Establishing a hierarchical structure, creating a pairwise comparison matrix, calculating eigenvalues and eigenvectors, carrying out consistency checks, and generating normalised weights are usually the main procedures [44,138].

This technique is frequently used in four primary processes for selecting a landfill site location [1]. The first step in its procedure is to identify the objective, such as finding suitable landfill sites. After that, it organises the problem and chooses relevant variables, like proximity to populated regions, protected areas, water bodies, and roads. After that, experts use Saaty’s scale for pairwise comparisons to assign weights and assess each criterion’s relative value. Following the computation of the weighted sum vector, the matrix is normalised to guarantee the consistency and dependability of the assigned weights. GIS and other MCDA approaches are frequently linked with the AHP to improve decision support. Each decision component can be evaluated independently using this method, and the results are then methodically merged to create a thorough spatial model [139].

Another technique offered by the AHP for assessing the consistency of judgements is the consistency ratio (CR), which is the ratio of the consistency index (CI) to the random index (RI) and depends on the number of criteria [20,96,140]. As described in [11], in pairwise evaluations, the CR shows how consistently decision-makers compare criteria and sub-criteria, guaranteeing the robustness and dependability of the weighting process. The following formula is used to determine this ratio [50,141,142]:

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{C}\mathrm{I}}}$$

(1)

where CR = consistency ratio, CI = consistency index, and RCI = random consistency index [10,11,108].

The value of CI and λ_max was calculated by Equation (2) [25,61,143]:

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{\lambda \mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{n}}{n-1}}$$

(2)

where λ_max = the largest eigenvalue of the pairwise comparison matrix, and n = the order of the matrix. Equation 3 shows how to compute the criteria weights and check consistency.

$$\mathrm{A}\mathrm{w}=\left({\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}·\mathrm{w}\right)$$

(3)

where A = pairwise comparison and

w = priority weight

Equation (4) shows the calculation of the principal (maximum) eigenvalue (λ) by applying the eigenvector technique [89,103,144,145]:

$$\lambda \mathrm{m}\mathrm{a}\mathrm{x}={\displaystyle \frac{1}{\mathrm{n}}}\sum _{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{\left(\mathrm{A}\mathrm{w}\right)\mathrm{i}}{\left(\mathrm{w}\right)\mathrm{i}}}$$

(4)

where n = number of criteria, Aw = the value of matrix weight multiplication, and W = weight [11].

After these calculations, a pairwise comparison matrix is deemed reliable if the consistency ratio (CR) is 0.1 or lower. Using Saaty’s scale, which comprises equal importance (1), moderate importance (3), strong importance (5), very strong importance (7), extreme importance (9), and intermediate values (2, 4, 6, 8) for compromise judgements, the CR measures how consistently decisions are made in pairwise comparisons [107,145]. To increase the reliability of the study, the comparisons should be re-evaluated or modified if the CR is greater than 0.1, which suggests a possible discrepancy. A solid decision-making process and trustworthy criterion weights are ensured by keeping the CR below 0.1. [53,146,147].

In addition, a standardised criterion scale is often used, with the weights summing to 1, which influences the evaluation scores of each spatial unit [24,108]. Each criterion’s relative relevance is represented by these weights, with more significant criteria having a bigger effect on the result. If the CR exceeds the allowed threshold, the comparison matrix needs to be updated to guarantee consistency. The calculated weights are integrated with standardised criteria maps using the weighted-sum method to create the final suitability map. The pairwise comparison matrices were judged to be sufficiently consistent since all of the values were below the acceptable limit of 0.10. As an example, the CR ratings ranged from 0.0077 to 0.09805, according to the findings of this literature review of 164 research studies. None of the studies exceeded 0.1.

3.7.2. Fuzzy Approaches and Distance Criteria

Fuzzy logic was first presented by Zadeh in 1965 as a method for suitability analysis. This approach deals with ambiguity and uncertainty in human perception and thought processes [148]. Fuzzy membership functions provide a helpful way for conveying uncertainty and standardising criteria in decision-making processes [37]. A fuzzy set is defined by a membership function that shows the degree of membership on a scale from 0 (no membership) to 1 (full participation) [17]. This method eliminates strict classification boundaries and is more adaptable than Boolean logic [98].

According to [149], decision-makers can better comprehend and assess landfill site selection procedures through fuzzy-based MCDA approaches. Contemporary analytical frameworks often integrate GIS, AHP, and Fuzzy Set Theory, and membership functions are used to determine how well particular traits match. As described in [4,13,115], membership is based on the chance of falling into a certain suitability class, categorised on a continuous scale from 0 to 1. A value of 0 indicates complete unsuitability, and a value of 1 indicates full suitability. Values between 0 and 1 indicate partial membership, with higher values denoting greater eligibility [135,148]. As noted in [89], this method is perfect for modelling complicated environmental data because of its flexibility, which allows users to define criteria and assign values within a continuous range.

The membership function’s numerical values can be changed to indicate different suitability levels. This approach offers a more precise, progressive evaluation of land suitability than rigorous methods. Areas with values close to 1 are believed to be the most suitable, whereas lower values imply decreasing potential for landfill location [24].

$$\mathrm{A}=\left\{\mathrm{x},\mu \mathrm{A}\left(\mathrm{x}\right)\right\} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h} \mathrm{x} \epsilon \mathrm{A}$$

where $\mu \mathrm{A}$ is the MF (membership of x in fuzzy set A) so that:

–

If x is fully an element of A, then $\mu \mathrm{A} = 1$

–

If x is not an element of A, then $\mu \mathrm{A} = 0$

–

If x is an element of certain degrees to A, then $0 < \mu \mathrm{A} \left(\mathrm{x}\right) < 1$ [24,149].

In general, a function value of one (1) denotes complete membership in a given set, whereas a value of zero (0) denotes non-membership. The degree of membership within the set is expressed as a value between 0 and 1 [104,140,150,151].

3.8. Multi-Criteria Decision Analysis (MCDA) Models

The siting of disposal facilities in waste management has been extensively studied using MCDA techniques. This reflects a transition from single-criterion to multi-criteria approaches that address conflicting requirements [12,134]. Because it provides an efficient way to evaluate and rank various choices based on environmental, social, and economic criteria, this method is frequently used in MSW landfill suitability assessments [151]. According to [103], it ranks options based on conflicting evaluation criteria, using decision-makers, evaluation standards, alternatives, and decision outcomes. Building on this framework, methods such as weighted sums are frequently used to score, evaluate, weight, and rank criteria pertinent to spatial problems [77,152,153].

When selecting criteria, local site considerations, expert assessments, national standards, and readily accessible data are considered. MCDA considers social, environmental, and technical factors when making spatial decisions. Decision-makers, options, evaluation standards, and decision outcomes are significant components. The effectiveness of this technique depends not only on the selected criteria but also on the procedure used to derive their weights. While expert judgement remains the predominant approach in landfill suitability studies, objective statistical weighting methods have also been proposed in other scientific disciplines [154]. A weighted least-squares framework has been introduced in which observation weights are estimated through an iterative procedure based on repeated median regression and resampling, thereby reducing the dependence on subjective weighting assumptions. Although developed for weighted regression analysis, this approach illustrates the broader potential of objective data-driven procedures for weight estimation.

The weighted sum approach is a popular MCDA technique that ranks, scores, assesses, and weights contributing factors according to their relative suitability [103]. As a result, these methods are often used to address difficult decision-making issues, such as landfill suitability [122,155]. When multiple alternatives are available, MCDA facilitates the selection of the most economical, socially acceptable, and environmentally responsible options by generating optimal rankings [144,156]. The siting process entails setting geographic criteria, identifying potential sites, gathering pertinent data, and conducting environmental impact studies before the final selection of a landfill site and land acquisition [152]. A variety of environmental, economic, and sociological factors must be considered while choosing a site. When paired with MCDA methods, GIS provides a powerful tool for creating such models. However, computational demands of complex analytical procedures can impact both processing time and computational efficiency [89,131,151,157]. Several techniques have been applied for landfill site selection, including the AHP, WLC, ANP, OWA, Fuzzy-AHP, Fuzzy-TOPSIS, F-ANP, and life cycle sustainability assessment (LCSA) [59,81,120].

Table 5. Comparative analysis of commonly used GIS-based MCDA methods for landfill site selection.

MCDA	Main Uses	Advantages	Limitations	Assumptions	Data Requirements	Uncertainty Treatment	Ref.
AHP	Weighs criteria using pairwise comparisons and prioritises alternatives.	Simple, transparent, consistency check (CR), widely used, easy GIS integration.	Subjective judgments; assumes criteria independence; many comparisons for large problems.	Criteria are independent, and experts provide consistent judgments.	Moderate	Low	[18,159,160]
WLC	Combines standardised criteria using assigned weights to generate suitability maps.	Simple, computationally efficient, widely used in GIS.	Sensitive to weights; compensatory effect may mask unsuitable criteria.	Criteria contribute linearly to suitability.	Low	Low	[38,161,162]
OWA	Aggregates criteria while considering decision-maker risk preferences.	Flexible; manages trade-offs; reduces decision uncertainty.	More complex weighting than WLC.	Risk preferences can be quantified.	Moderate	Moderate	[34,73,81]
ANP	Evaluates interdependent criteria using a network structure.	Models complex relationships among criteria; more realistic than AHP.	Computationally intensive; requires many pairwise comparisons.	Criteria are interdependent.	High	Moderate	[59,81]
CODAS	Ranks alternatives according to their distance from the negative ideal solution.	Strong discrimination among alternatives; robust ranking.	Relatively new; limited landfill applications.	Greater distance indicates better alternatives.	Moderate	Moderate	[157,158]
SAW	Computes weighted sums of normalised criteria values.	Easy to implement and interpret; computationally efficient.	Sensitive to assigned weights; ignores criterion interactions.	Criteria contribute additively.	Low	Low	[96,163]
VIKOR	Produces compromise rankings among conflicting criteria.	Balances conflicting objectives; identifies compromise solutions.	Sensitive to parameter settings.	Decision seeks compromise among alternatives.	Moderate	Moderate	[162]
TOPSIS	Ranks alternatives based on distance from ideal and negative-ideal solutions.	Simple, intuitive, effective ranking of alternatives.	Sensitive to normalisation and criterion weights.	Ideal and negative-ideal solutions exist.	Moderate	Moderate	[11,164,165]
Boolean Logic	Applies binary inclusion/exclusion rules using logical operators.	Very simple; rapid constraint mapping; effective for exclusion analysis.	Produces only binary outcomes; no gradual suitability.	Threshold values are absolute.	Low	None	[98,118]
FAHP	Extends AHP using fuzzy numbers to model uncertain judgments.	Better represents uncertainty and vagueness; improves weighting reliability.	More computationally demanding than AHP.	Expert judgments are represented by fuzzy sets.	Moderate	High	[116,135,165]
BWM	Determines criterion weights using the best and worst criteria.	Fewer comparisons; high consistency; efficient weighting.	Requires reliable identification of best and worst criteria.	Experts can identify extreme criteria accurately.	Moderate	Moderate	[147,166]
FUCOM	Derives weights while maintaining full consistency.	Minimal comparisons; high consistency; reduces inconsistency.	Less commonly applied than AHP or BWM.	Criteria priorities are consistently ordered.	Moderate	Moderate	[158]
ML	Learns spatial patterns to predict landfill suitability from geospatial data.	Captures nonlinear relationships; automates prediction; high accuracy.	Requires large, high-quality datasets; limited interpretability.	Training data represent real-world conditions.	High	High	[35,167]

shows that there is no single, widely used MCDA methodology for identifying landfill sites, since different approaches have advantages and disadvantages based on the decision-making situation. Because of its ease of use, clarity, consistency, and suitability for general landfill suitability assessment, AHP is the most popular method among those discussed. However, it is based on subjective expert judgements and implies independence among the criteria; the decision’s dependability in a complex ecological system is problematic. Although WLC and SAW are simple to use and computationally efficient, they consider compensatory relationships between criteria and rely on the weights applied to the criteria. While OWA, VIKOR, CODAS, and TOPSIS provide more sophisticated rating and compromise techniques for MCDA, ANP concentrates on the links between the criteria [128,158]. Additionally, by accounting for uncertainty in the expert evaluations, fuzzy logic-based techniques like FAHP guarantee a superior decision-making process. However, the BWM and FUCOM approaches have proven to be effective weighting strategies that produce consistent results with fewer pairwise comparisons [16,158]. Additionally, machine learning techniques can handle large-scale geographic data sets and complex nonlinear relations, but they require a lot of training. This suggests that when selecting an effective MCDA approach, it is necessary to consider study objectives, data availability, computational needs, decision complexity, and the degree of uncertainty associated with the landfill site selection problem.

3.8.1. Studies and Frequency of MCDA

The review shows that no single MCDA method or technique is used for landfill site selection. Instead, only a few well-known methods dominate. While some approaches are becoming more popular, they are not widely used at this point. The frequency analysis of the selected research studies was performed to find out the MCDA methods that are mostly utilised in landfill site selection. Based on the information provided in

Table 6. Frequency and percentage of MCDA methods used in GIS-based landfill site selection studies.

Rank	Method	Frequency (n)	Percentage (%)	Rank	Method	Frequency (n)	Percentage (%)
1	GIS	164	100.00	12	WASPAS	4	2.44
2	AHP	104	63.41	13	SWARA	3	1.83
3	WLC	69	42.07	14	ANP	2	1.22
4	Weighted Overlay	33	20.12	15	CODAS	2	1.22
5	Boolean Logic	29	17.68	16	BWM	2	1.22
6	FAHP	23	14.02	17	OWA	1	0.61
7	MCE	15	9.15	18	DEMATEL	1	0.61
8	Fuzzy Logic	13	7.93	19	COPRAS	1	0.61
9	SAW	12	7.32	20	FUCOM	1	0.61
10	TOPSIS	9	5.49	21	MARCOS	1	0.61
11	MULTIMOORA	5	3.05	22	VIKOR	1	0.61

, which includes the frequency and percentage of application of different techniques for the 164 research articles that undertook MCDA methods, while GIS was common for all studies, the analysis shows that AHP was the most widely used method among the reviewed articles since it has been utilised in 104 articles (63.41%). WLC accounted for 42.07%, while WOL and Boolean logic methods accounted for 20.12% and 17.68%, respectively. Other methods applied included FAHP, MCE, fuzzy logic, and others, which were combined and used in several studies. This table summarises several GIS-based multi-criteria decision analyses used in 164 studies, excluding review articles.

3.8.2. Land Suitability Assessment (Classical GIS Approaches)

Weighted overlay analysis facilitated strategic decision-making and produced the final suitability map by assigning weights or scale values to each criterion [51,168]. According to [135], they employed a five-stage process: identifying selection criteria, collecting relevant information and maps, creating thematic maps using rasterisation, supervised classification, and Euclidean distance analysis, applying the AHP model to determine criterion weights, and finally integrating GIS and AHP using reclassification and weighted overlay to enhance the suitability mapping [89]. Once the final weights are determined, GIS and MCDA are used to prepare criterion maps for spatial analysis and to identify suitable landfill sites. In the process of landfill optimisation, various environmental, social, and climatic factors are considered. Each criterion is first reclassified and assigned a weight based on its relative importance [169]. The weight of each criterion is then multiplied by its associated reclassified map to perform a weighted overlay analysis, typically using the raster calculator. The final landfill site suitability map, which highlights the best locations for landfill development, is created by integrating these weighted layers [57,85]. Land suitability was assessed using criteria evaluation results derived from GIS and MCDA. The resulting weighted overlay produced a suitability map for solid waste disposal sites, categorised according to the suitability scores [80,170]. According to the literature, weighted overlay analysis, parameter evaluation, and criteria selection are frequently used in landfill suitability assessment studies. Areas are usually categorised as not suitable, low suitability, moderately suitable, suitable, extremely suitable, or very highly suitable after analysis. In this approach, a suitability index that classifies land suitability for landfill sites is often developed using GIS and AHP [108]. In the broader context of designing multi-resolution spatial databases used for land suitability analyses, the extended structure of multi-resolution databases is of significant importance, as it enables the consistent storage and processing of data at various levels of detail [171]. According to [27], a variety of suitability ratings, such as extremely high, high, medium, low, and not suitable, are shown in scenario maps that determine the best disposal places. Model sensitivity can be assessed by varying the weights assigned to the criteria, thereby producing a range of suitable scenarios.

Similarly, ref. [170] used five categories to classify suitability: very low, low, moderate, high, and very high. A 1–5 ranking system is used in the majority of AHP-based evaluations, with 1 denoting not appropriate, 2 low suitability, 3 moderate suitability, 4 suitable, and 5 extremely suitable [101,112,172,173]. Alternative descriptive categories, including very poor, poor, fair, good, and very good, are used by certain researchers [98]. Furthermore, other studies use more specific classifications with more than five categories, including extremely unsuitable, very unsuitable, less unsuitable, unsuitable, moderate, less suitable, and extremely suitable. Terms like unsuited, disadvantageous, rather advantageous, advantageous, and very advantageous are examples of additional categories [43].

However, suitability is categorised differently under Boolean logic than under AHP. This method uses Boolean principles to assign a binary value of either zero (0) or one (1) to each area. The study area is divided into appropriate and inappropriate regions; regions with a value of 0 are excluded from the additional analysis. As a result, only regions with a score of 1 are deemed appropriate for landfill placement; locations with intermediate suitability levels are not included [6,98,110,113,117,121].

As illustrated in Figure 5, GIS was used to examine proximity and to produce thematic maps for each characteristic, following the collection of data on road networks, elevation, land use, streams, canals, and trains from several sources. The final landfill suitability map was subsequently created using weighted overlay analysis.

3.8.3. Suitability Maps and Environmental Criteria

The landfill suitability map was the final output of the GIS analysis, derived from the weighted evaluation of selected parameters [107]. A multi-criteria suitability map is a geographic tool that helps to determine which land areas are suitable for sustainable municipal landfill locations [23]. Similarly, refs. [86,87] state that the final suitability map generated by combining AHP and GIS techniques is the result of an analysis used to support a decision [63]. Most studies map the spatial suitability of landfills using weighted overlay analysis within a GIS platform. To produce an overall suitability map, the weighted overlay tool sums each cell across the weighted raster layers representing the different criteria [18,95]. In the context of managing post-mining and degraded areas, the use of ArcGIS and multi-criteria analyses to assess the suitability of land for various spatial functions has been discussed in detail, among other places, in studies of post-industrial areas in Upper Silesia [174,175]. Other Studies describe the landfill suitability map as the final output, visually representing the most suitable sites based on a comprehensive evaluation of all relevant variables. GIS tools are commonly used for data processing, enabling decision-makers to identify the most suitable locations [1,25,36,60]. The computational and organisational demands of processing such large, multi-layered spatial datasets have been documented in an industrial context [2,142].

To find appropriate landfill locations, researchers have often integrated GIS with MCDA. Studies on the suitability of land for MSW disposal frequently employ this integrated approach. For example, ref. [142] used GIS in combination with four MCDA methods to identify the best locations: the Boolean approach, Ratio Scale Weighing (RSW), AHP, and Straight Rank Sum (SRS). Their research demonstrated the effectiveness of combining MCDA and GIS, offering a reliable method for selecting suitable sites for the disposal of municipal solid waste (MSW). As seen in

Table 7. Summary of Methodologies, Aim, and Findings from Selected Studies on Landfill Suitability Assessment.

MCDA	Aim of the Study	Result	Ref
AHP + Boolean	To address the high waste generation resulting from rapid urban expansion To mitigate the overburdening of the existing landfill facility To overcome the lack of socioeconomically and environmentally acceptable decentralised landfill sites To improve inefficient traditional approaches to landfill siting under increasing urban pressure	Identification of landfill suitability areas Development of a comprehensive landfill suitability map to support optimal site selection Demonstration of the effectiveness of GIS-based technologies in identifying optimal landfill locations	[117]
AHP + WLC	To address inefficient municipal solid waste management and the lack of scientifically selected landfill sites in the Peshawar District To determine the relative importance of multiple and diverse landfill siting criteria To develop an integrated spatial analysis approach for landfill site selection To minimise environmental degradation and public health risks associated with poor landfill siting	Suitable landfill sites were identified to support effective municipal solid waste (MSW) disposal. Systematic weighting of criteria was conducted, enabling more reliable decision-making. Land suitability was classified as very highly suitable, highly suitable, moderately suitable, low suitability, and unsuitable.	[78]
AHP + WSM	Provide a systematic and quantitative method for evaluating and selecting suitable waste disposal sites Encourage the selection of sustainable disposal sites for efficient environmental and urban management. Describe the benefits and drawbacks of using Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS) while choosing a landfill location. Encourage sustainable urban waste management by implementing a reliable, transparent, and adaptable framework that reduces environmental and societal impacts.	A landfill suitability ranking map was developed to identify the most preferred sites for development. Site rankings were determined based on the model’s effectiveness The recommended landfill site was evaluated against alternative sites	[151]
AHP + Fuzzy	To address the growing solid waste problem caused by urban expansion and increasing municipal waste generation. To develop effective management strategies for the substantial daily waste generated To improve the existing unsanitary municipal solid waste management practices.	Classify the area into suitability classes as high, moderate, low, and unsuitable. Proposed alternative sites are recommended.	[75]
AHP	There is currently no suitable municipal solid waste disposal site in the area. The study aimed to identify the most appropriate landfill location in Hosanna town, following the determination of the optimal municipal solid waste (MSW) management strategy.	Factor maps were combined to create an overall suitability map Generate the suitability maps and give a rank The proposed sites are easily accessible and manageable	[105]
AHP	Despite Siliguri city’s limited space, solid waste generation is increasing due to population growth in the study area. Without adequate management, landfill sites are periodically developing around the city. The current disposal sites in the Siliguri Municipal Corporation planning area are inefficient and poorly planned.	Identification of suitable sites, with other sites ranked as alternatives. The final suitability map was prepared using overlay analysis	[91]
AHP + WLC	The existing landfill site is located on the main riverbed and has poor geological conditions. As a result, it poses a high risk of water pollution due to leachate infiltration. Hazardous and non-hazardous waste is currently disposed of in landfills without any form of segregation in the area. This lack of segregation makes it difficult to identify an appropriate landfill location that is socially acceptable, economically viable, and environmentally sound	Site suitability for landfill locations was first evaluated. Landfill suitability maps were then developed using GIS. Next, selected sites were compared based on land size, proximity to settlements, and distance from the city centre. Finally, weights were assigned to each criterion, and the sites were ranked to identify the most suitable landfill location.	[10]
AHP	In the study area, non-scientific site selection for solid waste management has led to hygiene and environmental problems. A variety of analytical tools and methods have been developed to address these issues, but they have not solved the problem in the study area. The aim is to identify appropriate locations for solid waste disposal.	According to the study, there are three types of landfill site suitability: extremely, moderately, and marginally acceptable. The results promote sustainable urban management by improving knowledge of landfill -site selection in ways that are both socially and scientifically acceptable.	[37]
AHP + WLC	Insufficient waste-collection facilities lead to open dumping and burning. Due to inadequate upkeep, current disposal sites have a detrimental impact on public health. Inadequate solid waste management is frequently linked to urban flooding in the area. Solid waste management is now a top priority and a significant task.	The area was identified as the best location for engineered landfills. Eleven sites were found to be highly suitable for landfills. The hydrogeological and physical environmental sub-models had a significant impact on the final suitability model.	[34]
AHP	For a 35-year planning period, the analysis suggests a method for identifying suitable disposal locations. Most research often focuses on current circumstances, which can lead to inadequate solutions as parameters evolve. To guarantee sustainable solutions, future projections should be incorporated into GIS frameworks utilising MCDA.	AHP is emphasised as a straightforward but efficient approach to solving complicated issues. When choosing disposal sites, the study suggests considering population estimates and future solid waste volumes. To identify sustainable landfill locations, which are crucial for effective urban planning, the approach uses dynamic datasets. suggests conducting field research at the proposed disposal sites.	[56]
AHP	The city’s challenges in municipal solid waste management are due to limited financial resources and a lack of technological expertise.	The study evaluated landfill site suitability based on multi-criteria analysis. Land was classified into four suitability categories: most suitable, suitable, less suitable, and unsuitable. From a financial perspective, the selected site is considered viable because it is closest to the main source of MSW generation.	[144]
AHP	The study aims to provide a scientifically robust and socially acceptable waste management solution, as urban solid waste poses significant health risks.	The concept divides land suitability into various groups by combining scientific accuracy with societal acceptance. The study offers stakeholders and decision-makers a framework for addressing the challenges of selecting disposal sites.	[90]
AHP	Solid waste management is a challenge for the city of Dodoma. Finding possible landfill locations is the study’s main goal. The site selection procedure uses GIS-based multi-criteria decision analysis.	The study prioritises the selected land into categories of most suitable, suitable, and less suitable for solid waste disposal, using AHP and GIS.	[102]
AHP	Urban waste disposal poses a major challenge for city planners, driven by rapid population growth and increasing urbanisation. Identifying suitable sites for solid waste disposal is essential for effective and sustainable waste management.	Remote sensing data were used to construct layer maps for these criteria. Unsuitable, less suitable, moderately suitable, extremely suitable, and very highly suitable were the classifications given to the detected landfill. The findings provide reliable spatial data to inform new landfill locations.	[123]
AHP + WLC	Sanitary landfills can effectively reduce environmental degradation when they are properly sited and managed. In Edo State, Nigeria, waste mismanagement is exacerbated by population growth, urbanisation, and industrialisation, resulting in significant environmental concerns.	Thematic maps were overlaid to create a waste site suitability map The findings reveal significant changes in land use and expanding built-up areas, The identified dumpsites pose health, socio-economic, and environmental risks.	[134]
AHP	To address challenges of identifying suitable landfill sites and managing solid waste in Nashik, Maharashtra, India.The existing landfill site is closing, necessitating the identification of new sites.	Based on projected population growth, the amount of landfill space required in the coming years was estimated. The results demonstrate the effectiveness of the proposed approach in assisting city planners to select environmentally sustainable landfill sites, as illustrated in the potential landfill site map.	[89]
AHP	Proper waste disposal on suitable terrain is crucial for effective waste management and requires careful landfill site selection to minimise environmental impacts.	AHP analysis showed that land use carried the highest weight, while fault distance had the lowest, with a consistency ratio of 0.05. The study area was classified into four landfill suitability zones, with the majority of regions deemed unsuitable. Three candidate sites were assessed, and only one was selected because the others were too small or located too close to the beach.	[176]
AHP	Many random waste disposal sites exist, leading to environmental, health, and operational challenges.	By classifying and rating raster maps according to the chosen criteria, a final landfill-siting map was generated in GIS. Unsuitable areas and their buffer zones were excluded to expedite the selection of potential landfill locations.	[137]
AHP + TOPSIS	Nashik, a city in western India, has seen rapid urbanisation over the last 10 years, characterised by increased industrial activity and a growing urban population. The city’s municipal solid waste production has significantly increased as a result of these changes.	The study employed a methodology to identify and prioritise potential landfill sites in Nashik, India. After evaluating 16 potential locations, L10, which is close to Adagaon, was selected as the best. The method of criterion analysis provides a systematic basis for selecting sustainable disposal sites.	[9]

, several other researchers have also used GIS–MCDA techniques in their studies.

The majority of studies created suitability maps using weighted overlay analysis in combination with GIS. While the classification methods differed, the most common classification used five classes (very low to very high suitability). The domination of the weighted overlay method demonstrates its wide application, indicating significant dependence on subjective criterion weighting; therefore, it is important to consider validity checks, sensitivity and uncertainty analyses.

3.9. Model Validation, Sensitivity and Uncertainty Analysis

The model results’ acceptability is confirmed by assessing their accuracy against the actual conditions of the selected landfill site. Because of this, validity, sensitivity, and uncertainty studies are crucial for assessing the robustness and dependability of GIS-based MCDA suitability models when weighted overlay maps and suitability zones are produced using thematic maps. These methods evaluate the accuracy of the model, compute uncertainties resulting from data and methodological assumptions, and investigate the implications of various weights. The main techniques for verifying and enhancing the reliability of landfill site selection models are summarised in the following subsections [9].

3.9.1. Model Validation

It is essential to assess the landfill site selection model to ensure its forecasts align with actual landfill areas, and this is carried out through various validation methods reported in existing studies. One of the methods applied was comparing predicted acceptable areas with existing landfill sites [165]. Assuming that current locations are suitable for disposing of solid waste, this method evaluates the spatial agreement between model results and real (operating) landfill sites. This approach is effective if the existing site was previously selected scientifically and fulfils social, environmental, and economic requirements [115,118]. Field verification, sometimes referred to as ground truthing, is another common validation technique utilised in several research investigations. Its purpose is to confirm that the projected site is comparable to the ground truth [3]. Expert judgement was frequently utilised to evaluate the justification behind specific locations [85,118]. Waste management specialists analyse possible sites based on their knowledge and local points of view; however, the lack of field data may cause assessments to be influenced by personal biases [74]. Cohen’s Kappa coefficient, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) analysis, and descriptive statistics techniques are common methods for providing an objective evaluation by measuring prediction accuracy and the agreement between model outputs and reference data [11,48,90]. Reviews indicate that validation is still underutilised. Most research relied only on expert judgement; very few included field verification, site comparisons, sensitivity analysis, uncertainty analysis, or ROC/AUC evaluation. This suggests that comprehensive validation frameworks are still uncommon, which may lower the predictability and dependability of landfill suitability assessments [48].

3.9.2. Sensitivity and Uncertainty Analysis

Sensitivity analysis, which assesses the robustness of a decision-making model by examining how changes to input parameters, criterion weights, or assumptions affect the final suitability outcomes, is a common way to quantify the impact of uncertainty on overall simulation/prediction. In GIS-based MCDA, sensitivity analysis is essential for assessing the reliability of suitability maps and promoting more transparent decision-making [99]. Uncertainty analysis, on the other hand, evaluates the influence of inconsistencies resulting from expert opinions, model assumptions, input data quality, and spatial datasets to ascertain the degree of confidence in model outputs. It evaluates how these errors influence the analysis model and affect the final suitability estimates, whereas sensitivity analysis examines the effects of modifying parameters [162,176,177]. Most studies on the selection of landfill sites continue to ignore sensitivity and uncertainty considerations. Few studies expanded their research to assess model resilience; most concentrated on computing the AHP consistency ratio (CR) to confirm the logical coherence of paired comparisons [155,178]. An overview of the validation methods applied in the previous research was provided in Figure 6.

The analyses performed revealed various validation methods. Based on the results, expert validation remains the most often used method at 73.8%; comparison with existing landfills appears in 52.4% of the reviewed articles as well, and field validation is used in 37.8% of studies. Method comparison occurs in 24.4% of the studies; sensitivity analysis is only performed in 16.5%, uncertainty analysis in 11.6%, and ROC/AUC validation method in 7.96%. In this regard, we can conclude that expert validation is the most widely applied method.

3.10. Practical Decision-Making Implications

According to this comprehensive review of the literature, GIS-based MCDA is the most often used framework for locating landfills because it incorporates a variety of features from the social, technological, economic, and environmental domains into a single spatial decision-making framework. The numerous techniques used, including AHP, FAHP, WLC and TOPSIS, highlight the significance of transparent and evidence-based landfill planning [58,164]. It also revealed significant differences in methodology, such as the choice of validation, weighting techniques, and establishing the locations of landfill sites. These variations impact models’ transferability and reproducibility for different regions, leading to significant inconsistencies. Evidence from this review evaluation indicates that most of the included research was done solely for the purpose of producing the suitability maps, without performing any necessary validations, sensitivity analyses, or uncertainty assessments. As a result, the decision-makers must take into account the reliability of the models utilised to create the suitability maps they employ.

3.11. Research Gaps and Future Directions

This comprehensive review of the literature indicates that GIS-MCDA in landfill site selection has significant methodological Gaps. First, despite the widespread use of GIS and AHP, few research investigations have employed advanced or hybrid MCDA approaches, which may reduce the errors and subjectivity associated with expert opinion. This implies that further application of these methods could enhance the reliability of landfill selection research. Second, the review found variations in evaluation criteria, exclusion criteria, and buffer distances, indicating that standards vary from region to region. Thirdly, most studies only employed the AHP consistency ratio and other techniques like field verification, comparison with existing locations, and Google Earth analysis to improve accuracy and reliability; only a small number of studies used validation techniques like ROC/AUC analysis [11]. Furthermore, only a few studies employed formal uncertainty analysis and sensitivity assessments, as Figure 6 illustrates. Last but not least, a significant number of the examined studies on landfill site selection relied on static spatial datasets and conventional GIS-MCDA methodologies, with minimal integration of advanced technology and high-resolution remote sensing. The majority of research employs conventional approaches with expert judgement, which ignores the potential advantages of AI technologies. Integrating these technologies with GIS-MCDA and the latest datasets may lead to more effective landfill selection methods [16,156,179]. Future studies should concentrate on hybrid frameworks that include validation, sensitivity analysis, and uncertainty assessment in order to improve model reliability and applicability, enhancing the precision, transparency, and utility of landfill site selection models.

4. Conclusions

This review presents both quantitative and qualitative analyses of 175 peer-reviewed studies conducted between the years 2016 and 2026 on landfill site selection for solid waste disposal. Most existing reviews were limited in their detailed investigation and were descriptive, but this review was organised based on a better methodological framework to support the design of landfill site selection using GIS and MCDA. It assessed the distribution of waste management studies through landfills, methodological development status, the use of decision-making support systems, validation techniques, etc., over the last ten years. The main conclusions are described below.

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Research on landfills for waste management is still widespread in many parts of the world, and there has been an annual increase in studies in this study area, although their geographical distribution differs by country.

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The majority of the research focuses on creating a suitability map and establishing suitability assessments before validating the selected landfill site location.

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Methodological developments have progressed from conventional Boolean and weighted overlay methodologies to the current application of hybrid GIS-MCDA systems, characterised by wider application of optimisation algorithms, Fuzzy AHP, Fuzzy TOPSIS, MULTIMOORA, etc.

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When integrated with GIS, the synergistic use of MCDA, which has recently increased in landfill site selection, was quite remarkable and deserves to be supported for further research.

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Most studies basically depend on low-resolution DEMs and unreliable datasets, so increasing the accuracy and reliability of landfill-site selection will be enhanced by high-resolution DEMs, current geospatial datasets, and UAV-derived data.

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To enhance transparency and trust in landfill siting models, it is essential to incorporate validation techniques, consistency checks, sensitivity, and uncertainty analysis.

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Integrating GIS-MCDA with AI technologies like Random Forest, Machine Learning, ANN, and optimisation advancements can improve accuracy and reliability while minimising subjectivity.

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Additionally, adopting a consistent threshold value for important criteria will enhance environmental, social, geological and geomorphological protection and reduce the diversity in criterion consideration among study areas within the same country.