Integrated Fuzzy-GIS Approach for Optimal Landfill Site Selection in Tabuk, Saudi Arabia, Supporting Sustainable Development Goals

Abstract

The rapid urban growth in Saudi Arabia has intensified challenges in sustainable solid waste management, particularly in selecting suitable landfill sites that minimize environmental risks and protect public health. Tabuk Province, located in the northwest of the Kingdom, represents a region where arid climatic conditions, fragile ecosystems, and increasing urbanization make landfill sitting highly complex. Traditional decision-making approaches often struggle to capture uncertainties in expert opinions and spatial data, leading to less reliable outcomes. While Geographic Information Systems and Multicriteria Decision-Making have been applied to this field, the explicit integration of fuzzy logic remains limited, especially in arid regions. This study addresses this gap by combining the Fuzzy Analytic Hierarchy Process with Geographic Information Systems to establish a more robust framework for landfill site selection in Tabuk. Seven critical criteria were considered, including distance from major roads, airports, urban centers, coastlines, wetlands, and protected areas, with expert assessments analyzed through fuzzy reasoning to improve decision reliability. The results generated a spatial suitability map highlighting priority zones for landfill development, particularly in the western and southwestern areas of the province, where environmental sensitivity is lower and accessibility to infrastructure is greater. The findings emphasize that proximity to urban areas and road networks are dominant factors influencing suitability. The novelty of this study lies in its methodological integration, which enhances transparency, adaptability, and objectivity in landfill sitting. By promoting environmentally responsible waste management, the framework directly supports the Sustainable Development Goal of Good Health and Well-Being and the Sustainable Development Goal of Sustainable Cities and Communities, ensuring safer urban development and healthier living conditions. Moreover, the approach is transferable to other arid and semi-arid regions, offering valuable insights for countries facing similar challenges in sustainable urban planning.

1. Overview

Effective solid waste management (SWM) is essential for safeguarding public health, protecting the environment, and promoting sustainable development [1,2]. It is also a crucial part of the “Saudi VISION 2030” goals for environmental sustainability and smart urban planning [1,2]. As urban populations expand and consumption patterns intensify, cities generate increasing volumes of solid waste (SW) that must be managed properly to prevent land, air, and water pollution [3]. The disposal of these wastes in landfills remains a common and practical solution, especially in regions where other technologies such as incineration or advanced recycling systems are limited or economically unfeasible [3,4]. However, the success of landfill systems depends heavily on the selection of appropriate sites, which must meet strict environmental, technical, and social criteria [5]. Poorly chosen locations can lead to long-term contamination of soil and groundwater, degradation of surrounding ecosystems, and opposition from local communities [6,7,8].

In northwestern Saudi Arabia (SA), the challenge of selecting suitable landfill sites is particularly complex. This region covers a vast geographical area and is characterized by ecological diversity that includes arid deserts, mountainous areas, and coastal zones [9]. Tabuk Province also features several growing urban centers and is a focus of national development efforts under the country’s broader strategic plans. As such, the management of municipal SW in this area requires a forward-looking approach that considers both environmental fragility and the anticipated rise in population and economic activity.

The present study proposes a methodological framework for identifying and evaluating suitable landfill sites across the Tabuk region. The framework incorporates spatial and non-spatial parameters using a combined decision-making tool based on the Fuzzy Analytic Hierarchy Process (FAHP) and Geographic Information Systems (GISs) [10,11,12]. The aim is to improve the accuracy, transparency, and environmental soundness of landfill siting decisions. This can be achieved by relying on advanced tools that help planners navigate the complexity of conflicting objectives and uncertain data.

The process of selecting landfill sites is widely recognized as a multi-criteria decision-making problem. This is because it involves balancing various objectives, such as minimizing environmental risk, ensuring technical feasibility, optimizing costs, and maintaining social acceptability. For example, sites need to be sufficiently far from residential areas to reduce nuisance and health risks, but close enough to transportation infrastructure to ensure operational efficiency. Similarly, proximity to water bodies must be avoided to prevent leachate contamination, but the site must remain accessible to waste collection vehicles and support systems. Traditional methods of site selection have tended to rely on simplified decision rules or qualitative assessments [13].

While useful in the early stages, the above approaches often fail to account for the uncertainty and subjectivity involved in evaluating multiple conflicting criteria. Moreover, they may overlook important interactions between variables and can result in biased or inconsistent recommendations. To address these limitations, the integration of fuzzy logic with spatial analysis has gained significant attention in the field of environmental planning [13,14,15,16,17]. FAHP is a variation in the widely used Analytic Hierarchy Process (AHP), adapted to handle the ambiguity and vagueness associated with human judgment [14,18,19,20,21,22]. Instead of assigning crisp numerical values to each criterion, FAHP allows decision-makers to express their preferences in linguistic terms, such as “equally important” or “moderately more important,” which are then translated into fuzzy numbers. This strategy captures the degree of confidence or uncertainty in expert assessments and leads to more robust and realistic prioritization of criteria [14,23].

Once the relative weights of each factor are determined through the FAHP model, the next step is to apply these weights to spatial data using GIS [14]. GIS provides a powerful platform for collecting, managing, analyzing, and visualizing geospatial data [24,25,26,27]. It allows users to overlay multiple layers of information, such as land use, soil type, elevation, slope, road networks, population distribution, water sources, and protected areas [28,29]. Each of these layers can be evaluated according to the weighted criteria from FAHP to generate a composite suitability map [14] that highlights areas best meeting the technical and environmental requirements for landfill development.

In the Tabuk context, the use of FAHP and GIS offers several advantages [17,30,31,32]. First, it provides a scientifically grounded and repeatable method for assessing potential landfill sites across a wide and ecologically varied region. Second, it facilitates collaboration among stakeholders by making the decision-making process transparent and evidence based. Third, it reduces the risk of environmental degradation by ensuring that sensitive ecosystems, aquifers, and population centers are adequately protected from potential negative impacts [17,30,33].

The proposed methodology begins with the identification of relevant criteria for landfill siting. These include environmental parameters such as proximity to surface water and groundwater, soil permeability, slope, and land cover type [17,30]. Technical criteria such as accessibility via roads, distance from urban and rural settlements, and proximity to power and water infrastructure are also considered [33]. Furthermore, socioeconomic and regulatory factors such as land ownership, future land use plans, population density, and community acceptance play a crucial role in site feasibility. The criteria are organized into a hierarchical structure, with the over-arching goal placed at the top, followed by the main categories of evaluation, and finally the individual sub-criteria [30].

Next, through a series of pairwise comparisons, expert judgments are collected to determine the relative importance of each criterion. These comparisons are processed through fuzzy logic to calculate the final weight of the factors. A consistency check is also performed to ensure that the judgments are logically coherent. In the GIS phase, spatial datasets corresponding to each criterion are collected and processed. These may include satellite imagery, digital elevation models, land use maps, hydrological data, infrastructure maps, and demographic data. Each raster or vector layer is standardized according to its influence on landfill suitability [30]. For example, areas closer to water bodies may receive lower suitability scores due to contamination risk, while areas with moderate slope and access to major roads may receive higher scores. Using weighted overlay analysis, a final composite suitability map is generated, categorizing the land into zones ranging from highly suitable to completely unsuitable [17,30].

Similar FAHP–GIS methodologies have been successfully applied to landfill site selection in other arid and semi-arid regions. For instance, Armanuos et al. [34] applied FAHP integrated with GIS to identify optimal landfill locations in the Nile Delta, Egypt, considering environmental, socio-economic, and technical criteria. Pasalari et al. [35] employed a hybrid AHP–Fuzzy GIS approach in Shiraz, Iran, to prioritize landfill sites while explicitly addressing uncertainty in expert judgments. Abdulhasan et al. [36] used GIS combined with fuzzy logic and AHP for landfill planning in Nasiriyah, Iraq, demonstrating improved transparency and replicability in solid waste management decision-making.

The final output allows decision-makers to visualize spatial trade-offs and identify priority areas for further investigation. It also serves as a basis for conducting environmental impact assessments, engaging stakeholders, and integrating landfill planning into broader urban development strategies. The FAHP-GIS model can be updated periodically to reflect new data, changing regulations, or shifts in community preferences, making it a flexible tool for long-term planning.

In the case of Tabuk, this study contributes to the goal of creating a more sustainable waste management system. By aligning landfill siting decisions with environmental, social, and technical criteria, the region can minimize ecological harm while meeting its growing waste disposal needs. The approach encourages informed decision-making and responsible land use that is consistent with national and international commitments to environmental protection and sustainable urban development. The main objective of this research is to develop a scientifically grounded and context-sensitive framework for landfill site selection in the Tabuk region of Saudi Arabia.

Our effort begins with the identification and definition of a comprehensive set of spatial and non-spatial criteria that encompass environmental, technical, economic, and social dimensions relevant to solid waste (SW) disposal planning. By using expert input, these criteria are then evaluated and ranked through FAHP. This method is especially effective in capturing subjective judgments and accommodating uncertainty, which is a common feature of real-world decision-making. Following the criteria prioritization, relevant geospatial data layers are collected and processed within a GIS environment. These data represent physical, infrastructural, and ecological features of the region. The weights derived from FAHP are applied to these layers to calculate the spatial suitability of different zones within the study area, resulting in the creation of a suitability map that highlights the most appropriate areas for landfill development.

The final product is a practical decision-support tool that assists urban planners and municipal authorities in making informed and transparent choices. It allows them to minimize environmental risks, consider future growth, and align landfill development with broader regional planning goals. This research responds to the growing need for integrated SWM systems in rapidly developing regions like Tabuk, where increased population and industrial expansion place additional strain on land use and environmental resources. The study also provides a structured and repeatable methodology for landfill siting, one that can be updated as new data or priorities emerge. Ultimately, this work contributes to improved environmental governance by enhancing the quality of decisions related to waste management infrastructure.

The originality of this study lies in its methodological integration, regional focus, and multidimensional scope. It is the first known application of FAHP combined with GIS tools for landfill site selection specifically adapted to the Tabuk region. This is significant, because the study considers the unique characteristics of the area, such as its arid climate, the sensitivity of its groundwater resources, the distribution of its population, and its complex terrain. These local conditions require customized criteria and weights, making the model both specific and transferable.

Another key aspect of the study’s originality is the research’s broad and inclusive approach to evaluation. While many prior models tend to emphasize only technical or environmental factors, this study incorporates a wide range of dimensions, including engineering feasibility, infrastructure availability, ecological integrity, land use compatibility, economic viability, and community acceptance. This level of integration ensures a more balanced and comprehensive assessment, which is vital for gaining public support and ensuring long-term success.

Furthermore, the research contributes methodological refinement to the field of decision analysis. By using fuzzy logic to represent imprecision in expert judgments, the model overcomes a common limitation in many multi-criteria methods that rely on exact numeric values. This makes the prioritization of criteria more realistic and reflective of the inherent uncertainty in planning decisions. The results are also easier to communicate and justify to stakeholders. As well, the study produces a spatially explicit output in the form of a landfill suitability map. This map translates complex analytical outputs into visual and actionable information that can be used directly by planners and policymakers. It enhances transparency and facilitates public participation by making the results accessible and understandable.

To address the complex challenge of selecting optimal landfill sites in the Tabuk region, this study is guided by a set of research questions and corresponding hypotheses. The research questions focus on which geographic areas in Tabuk are most suitable for landfill development based on environmental, economic, technical, and social criteria. They also look at how these factors influence the prioritization of landfill sites under conditions of uncertainty, and how the integration of FAHP and GIS could improve decision-making for sustainable SWM.

Based on the above questions, the study tests the following hypotheses. First, areas with lower population density and greater distance from urban centers are more suitable for landfill development. Second, environmental and social criteria exert greater influence on site suitability compared to economic or technical factors. And third, that integrating FAHP with GIS provides a more consistent and reliable framework for landfill site selection than GIS alone. By explicitly addressing these hypotheses and the foregoing questions, the study establishes a structured research framework that links objectives, methodology, and expected outcomes. The findings are thus ensured to be evidence-based and relevant to regional planning, as well as to the “Saudi VISION 2030” goals for sustainable urban development and waste management. The integration of environmental, technical, social, and economic criteria in landfill site selection not only addresses local challenges but also aligns with global sustainability priorities [20]. Effective solid waste management is a key contributor to public health by preventing soil, air, and water contamination, reducing the risk of disease, and minimizing the exposure of communities to hazardous pollutants [22,37]. In this regard, the proper siting of landfills directly supports the Sustainable Development Goal of Good Health and Well-Being by safeguarding human populations from the environmental hazards associated with poor waste disposal practices [38]. Furthermore, sustainable landfill planning contributes to the Sustainable Development Goal of Sustainable Cities and Communities by ensuring that urban expansion and municipal development proceed in an environmentally responsible and socially acceptable manner [39]. Selecting optimal landfill sites in the Tabuk region, a diverse and ecologically sensitive area, requires a methodological framework that can accommodate uncertainty, integrate multiple criteria, and produce results that are transparent and replicable. The Fuzzy Analytic Hierarchy Process combined with Geographic Information Systems offers a practical solution to these requirements by allowing expert judgments to be expressed in linguistic terms and capturing the inherent ambiguity in decision-making. This approach enables urban planners and municipal authorities to balance competing objectives, such as minimizing environmental risk, maintaining technical feasibility, ensuring cost efficiency, and gaining community acceptance. By adopting such a scientifically grounded and flexible framework, the Tabuk region can ensure that landfill development is aligned with population growth, economic activities, and regional planning objectives, while simultaneously protecting sensitive ecosystems and water resources. Importantly, the methodology developed in this study is transferable and can be adapted to other arid and semi-arid regions facing similar urban and environmental pressures, extending the benefits beyond the immediate study area. In addition, this research reinforces the broader role of integrated decision-support systems in promoting sustainable urban governance, evidence-based planning, and responsible land use management. By explicitly connecting the FAHP-GIS methodology with global sustainability targets, the study highlights how local interventions in waste management can have wider implications for achieving international goals. Ultimately, these additions emphasize the importance of embedding public health and urban sustainability considerations within environmental planning processes, demonstrating that systematic and data-driven approaches to landfill site selection can directly contribute to achieving the Sustainable Development Goals of Good Health and Well-Being and Sustainable Cities and Communities, while providing a robust and adaptable framework for long-term sustainable development in the Tabuk region and comparable settings.

2. Materials and Methods

The present research forms a continuation of an infrastructure planning project led by the University of Tabuk, with a focus on enhancing municipal SWM strategies across the region. In an earlier phase, a structured decision-making framework was developed using FAHP to determine the relative importance of key landfill sitting criteria. Five principal categories were identified: economic aspects (C1), environmental sensitivity (C2), ecological and management considerations (C3), social factors (C4), and engineering and technical performance (C5). Based on expert assessments and regional needs, the model assigned the following weights: 40% for engineering and technical factors, 33% for ecology and management, 16% for social impact, 8% for economic feasibility, and 3% for environmental risk. These weights served as the analytical foundation for evaluating spatial suitability in the current phase of the study.

To analyze how these weighted criteria influence the geographic distribution of potential landfill sites, a case study was developed for the Tabuk region. Geographically, the study area spans from 34.6° to 39.9° east longitude and from 24.6° to 29.97° north latitude, covering an area of roughly 146,072 km². The region features an arid climate with low annual rainfall averaging about 40 mm, as well as sensitive ecosystems along its Red Sea coastline. The population of Tabuk Province is around 900,000, and rapid urbanization is being driven by agricultural expansion and national projects such as NEOM. These dynamics place increasing pressure on land use and call for proactive landfill site planning that minimizes environmental impact while ensuring long-term infrastructure viability.

Spatial data necessary for the analysis were acquired and processed using QGIS software (version 3.22.14, QGIS Development Team, Open Source Geospatial Foundation Project, Beaverton, OR, USA). The collected data layers included information on land use, elevation, and road and transportation networks, as well as information on proximity to water bodies, coastlines, protected areas, and urban settlements. All data were standardized and prepared for integration into a GIS. The FAHP-generated weights were applied to these layers within the GIS environment to conduct a weighted overlay analysis. This approach facilitated the generation of a landfill suitability map, reflecting varying levels of suitability across the region based on combined criteria.

Fuzzy logic played a central role in enhancing the decision-making framework. It offers a flexible and robust methodology for addressing uncertainty and imprecision, which are often inherent in spatial planning problems. By incorporating fuzzy sets and linguistic variables such as “near,” “moderate,” and “far,” expert opinions that are difficult to quantify were translated into numerical scales. This allowed for a more accurate assessment of suitability, particularly in cases where crisp boundaries and precise values are not available. The integration of fuzzy logic enabled the model to represent real-world ambiguity in a structured and computationally manageable form.

The combination of FAHP and GIS led to the development of a comprehensive and dynamic decision-support system. The spatial analysis capabilities of GIS made it possible to merge various geospatial layers, calculate spatial relationships, perform distance-based evaluations, and overlay multiple criteria efficiently. By integrating FAHP membership functions into GIS operations, the site selection process became more adaptable to changing inputs and capable of delivering visual outputs for enhanced decision-making. The resulting suitability maps provided a clear representation of optimal and suboptimal areas, helping stakeholders to better understand trade-offs and constraints.

The evaluation of candidate landfill locations was conducted using seven geospatial parameters based on distance from major roads, airports, urban settlements, coastline, wetlands, waterways, and environmentally protected zones. Each of these seven parameters corresponds to more than one of the five primary selection criteria, thereby characterizing the problem as a multi-criteria decision-making challenge. For instance, economic feasibility is strongly linked to accessibility, which favors proximity to main roads. Social concerns are shaped by how close the site is to urban zones or water sources, given the potential for odor and contamination, and environmental protection requires careful avoidance of sensitive zones such as wetlands, aquifers, and coastal margins.

The interaction of these parameters was analyzed using spatial tools in QGIS to assess constraint conditions and identify suitable locations. Tabuk, being a coastal and agriculturally active region with designated wildlife areas, presents particular challenges. Risks associated with leachate infiltration, groundwater contamination, and air pollution informed the definition of minimum buffer distances from key features. These buffers were incorporated into the GIS layers to filter out unsuitable zones.

The integrated FAHP-GIS model enabled the simultaneous evaluation of multiple conflicting and interrelated criteria under uncertain conditions. The approach reduced the time, cost, and effort usually associated with manual site evaluation and provided planners with a spatially explicit tool to support transparent and evidence-based landfill siting decisions in the Tabuk region (

Table 1. Evaluation Parameter Constraints and Data Sources.

Parameter	Abbreviation	Constraint Value	Mapping of Criteria	Data Type	Data Source
Distance to main road network	P1	1 km ≤ distance ≤ 3 km	Environmental: Reduce soil pollution and moisture rise. Economic: Accessibility and cost-effectiveness. Social: Employment opportunities.	Vector	OSM (OpenStreetMap)
Distance from airports	P2	distance ≥ 5 km	Environmental: Avoid dispersal of air pollutants. Safety regulations near airports.	Vector	OSM
Distance from urban areas	P3	5 km ≤ distance ≤ 10 km	Environmental: Minimize air pollution, odor, and noise. Social: Improve public acceptance, reduce health risk.	Vector	OSM
Distance from coastline	P4	distance ≥ 5 km	Technical: Mitigate impact of coastal weather. Environmental: Prevent saltwater intrusion and leachate pollution. Risk management: Coastal disaster response.	Vector	OSM
Distance from wetlands	P5	distance ≥ 300 m	Environmental: Prevent eutrophication and leachate pollution. Social: Preserve landscape and public trust.	Vector	OSM
Distance from waterways (rivers, wadis)	P6	distance ≥ 300 m	Environmental: Protect groundwater and surface water. Social: Promote equity and water conservation.	Vector	HydroSHEDS
Distance from protected areas	P7	distance ≥ 500 m	Environmental: Preserve ecosystems. Legal: Comply with national regulations and obtain necessary permits.	Vector	OSM, Google Earth

).

Figure 1 illustrates the procedures that were utilized to maximize the selection of disposal sites for SW in the area under investigation. This figure presents an integrated framework combining FAHP and GIS for landfill site selection. The FAHP analysis begins with expert opinions to build and validate a consistent AHP matrix, followed by the calculation and normalization of fuzzy weights. Simultaneously, the GIS analysis involves processing spatial datasets such as land use, roads, wetlands, and protected areas to generate proximity distance layers. FAHP-derived weights are assigned to these layers, which are then rescaled, evaluated for criterion membership, and overlaid.

The final output identifies optimal potential sites for landfill development. When the GIS analysis reached a more advanced stage, the FAHP analysis would be incorporated into it. To ascertain the relative importance of each geospatial parameter within the context of the five primary criteria that were taken into consideration, a fuzzy analysis was carried out. The fuzzy analysis used the geometric mean method, as shown in Equations (1)–(6) [40,41]:

$$\mathit{Fuzzy} \mathit{G}\mathit{e}\mathit{o}\mathit{m}\mathit{e}\mathit{t}\mathit{r}\mathit{i}\mathit{c} \mathit{M}\mathit{e}\mathit{a}\mathit{n}, {\mathit{r}}_{\mathit{i}}={\left({\prod }_{\mathit{j}=1}^{\mathit{n}}{\mathit{A}}_{\mathit{j}}\right)}^{{\displaystyle \frac{1}{\mathit{n}}}}$$

(1)

Fuzzy Weight, w_i = r_i ⨂ (r₁ ⊕ r₂ ⊕ … r_n)⁻¹

(2)

$${\mathit{D}\mathit{e}\mathit{f}\mathit{u}\mathit{z}\mathit{z}\mathit{i}\mathit{f}\mathit{i}\mathit{e}\mathit{d} \mathit{W}\mathit{e}\mathit{i}\mathit{g}\mathit{h}\mathit{t}, \mathit{M}}_{\mathit{i}}={\displaystyle \frac{\mathit{l}+\mathit{m}+\mathit{u}}{3}}$$

(3)

$$\mathit{N}\mathit{o}\mathit{r}\mathit{m}\mathit{a}\mathit{l}\mathit{i}\mathit{z}\mathit{e}\mathit{d} \mathit{W}\mathit{e}\mathit{i}\mathit{g}\mathit{h}\mathit{t}, {\mathit{N}}_{\mathit{i}}={\displaystyle \frac{{\mathit{M}}_{\mathit{i}}}{\sum {\mathit{M}}_{\mathit{i}}}}$$

(4)

$$\mathit{C}\mathit{o}\mathit{n}\mathit{s}\mathit{i}\mathit{s}\mathit{t}\mathit{e}\mathit{n}\mathit{c}\mathit{y} \mathit{I}\mathit{n}\mathit{d}\mathit{e}\mathit{x}, \mathit{C}\mathit{I}={\displaystyle \frac{{\mathit{\lambda }}_{\mathit{m}\mathit{a}\mathit{x}}-\mathit{n}}{\mathit{n}-1}}$$

(5)

$$\mathit{C}\mathit{o}\mathit{n}\mathit{s}\mathit{i}\mathit{s}\mathit{t}\mathit{e}\mathit{n}\mathit{c}\mathit{y} \mathit{R}\mathit{a}\mathit{t}\mathit{i}\mathit{o}, \mathit{C}\mathit{R}={\displaystyle \frac{\mathit{C}\mathit{I}}{\mathit{R}\mathit{I}}}$$

(6)

where A_n = the fuzzy number of the nth parameter, n = the number of parameters, r_i = the fuzzy geometric mean value of the ith parameter, λ_max = the principal eigenvalue, and RI = random inconsistency index. This index is directly dependent on the number of analyzed parameters (n) and can be deduced from

Table 2. Random Inconsistency Index Values.

n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

.

Aggregated results of fuzzy weights were used to create adjusted weights of the geospatial parameters studied, as per the influencing criteria. The normalized aggregated weight (w_ag) is calculated in Equation (7):

$$w_{ag}={\sum }_{j=1}^m{N}_i.x_j$$

(7)

where x_j = score of the jth criterion and m = number of criteria.

The geospatial analysis commenced with the collection of foundational datasets from multiple authoritative sources. Vector-format land use data were acquired from the OpenStreetMap (OSM) platform, while long-term satellite-derived datasets related to wind patterns, elevation, and land cover were sourced from NASA’s Earth observation repositories. High-resolution hydrological data, including active stream networks, were obtained in raster format from the HydroSHEDS database, structured as 5 × 5 degree tiles. To ensure consistency in processing, all raster datasets were standardized to a spatial resolution of 12.5 by 12.5 m. Any vector or polygon-based layers were converted to raster format using QGIS software to maintain uniform resolution across all inputs.

Once the datasets were prepared, all spatial layers were aligned to a common geographic reference framework, specifically the WGS 84/UTM zone 37N coordinate system (EPSG:32637). Proximity analyses were then carried out across each layer to generate distance maps from key spatial features. These raw distance values were subsequently normalized through reclassification into equal proximity intervals to facilitate comparative evaluation. This process yielded seven refined geospatial layers, each representing a key spatial variable relevant to the landfill site selection process.

Using the structured criteria previously defined under the FAHP framework, each of the reclassified spatial layers was associated with corresponding membership functions. These values were used to assign degrees of suitability based on the relative influence of each parameter. The constraint thresholds defined in the decision matrix were then applied to categorize the layers into five distinct membership levels, corresponding to different suitability grades.

The final phase of the analysis involved integrating the FAHP-derived weights with the normalized spatial layers using raster-based calculations in QGIS. This composite analysis resulted in the generation of a landfill suitability map for the Tabuk region. The map ranked land areas based on their compatibility with SW disposal criteria, thereby providing a clear visual tool for identifying optimal zones for landfill development.

3. Results and Discussion

3.1. Fuzzy Analysis

For each site selection criterion, we created a pairwise comparison matrix to evaluate the relative importance of the parameters studied. In this study, Triangular Fuzzy Numbers (TFN) were applied to rank the opinions of fifteen experts with specialization in environmental engineering, regional planning, and civil engineering. The expert panel consisted of both local decision-makers from the Tabuk region and academic professors in civil and environmental engineering, ensuring representation of both practical and theoretical perspectives. The use of TFN enabled the quantification of subjective assessments regarding the relative importance of identified environmental and planning factors, providing a structured, reproducible, and transparent approach. By expanding the expert panel to 15 participants, the analysis gains robustness and reduces the potential influence of individual bias, supporting the reliability of the derived priority rankings for informed decision-making in the Tabuk region. TFN is expressed as A = (l, m, u), where l is the lower value, m is the mean, and u is the upper value of the triangular fuzzy bound. A reciprocal of a TFN is denoted by: $A^{-1}=\left({\displaystyle \frac{1}{u}},{\displaystyle \frac{1}{m}},{\displaystyle \frac{1}{l}}\right).$

The geometric mean method was used to calculate the fuzzy weight of each parameter, after which the weights were defuzzied and normalized to find the crisp weights of the evaluated parameters. Pairwise comparison matrices were developed to determine the relative priority of the seven geospatial parameters under each of the five main criteria. All these matrices were checked for consistency, with a random index (RI) of 1.32 for a seven-parameter matrix, as presented in Table 2.

Table 3. Pairwise Comparison Matrix for the Economic Criterion.

Parameter	P1	P2	P3	P4	P5	P6	P7
P1	1	9	1/2	9	9	9	8
P2	1/9	1	1/9	1/2	1/3	1/3	1/9
P3	2	9	1	9	8	8	5
P4	1/9	2	1/9	1	1/2	1/2	2
P5	1/9	3	1/8	2	1	1	1
P6	1/9	3	1/8	2	1	1	1
P7	1/8	9	1/5	1/2	1	1	1
Consistency		λmax = 7.75, CI = 0.125 CR = 0.095 < 0.1 Reasonably consistent matrix.

,

Table 4. Pairwise Comparison Matrix for Environmental Criterion.

Parameter	P1	P2	P3	P4	P5	P6	P7
P1	1	1/2	1/9	1/9	1/8	1/8	1/9
P2	2	1	1/9	1/9	1/9	1/9	1/9
P3	9	9	1	2	1/2	1/2	1/2
P4	9	9	1/2	1	1/2	1/2	1
P5	8	9	2	2	1	1	1
P6	8	9	2	2	1	1	1
P7	9	9	2	1	1	1	1
Consistency		λmax = 7.28, CI = 0.046 CR = 0.035 < 0.1 Reasonably consistent matrix.

,

Table 5. Pairwise Comparison Matrix for the Ecological and Management Criteria.

Parameter	P1	P2	P3	P4	P5	P6	P7
P1	1	2	1/5	1/7	1/6	1/6	1/2
P2	1/2	1	1/9	1/7	1/8	1/8	1/7
P3	5	9	1	7	2	2	2
P4	7	7	1/7	1	1/4	1/4	2
P5	6	8	1/2	4	1	1	2
P6	6	8	1/2	4	1	1	2
P7	2	7	1/2	1/2	1/2	1/2	1
Consistency		λmax = 7.69, CI = 0.115 CR = 0.087 < 0.1 Reasonably consistent matrix.

,

Table 6. Pairwise Comparison Matrix for the Social Criterion.

Parameter	P1	P2	P3	P4	P5	P6	P7
P1	1	2	1/7	1/8	1/3	1/3	1/5
P2	1/2	1	1/9	1/9	1/7	1/7	1/5
P3	7	9	1	2	3	3	2
P4	8	9	1/2	1	7	7	2
P5	3	7	1/3	1/7	1	5	1/2
P6	3	7	1/3	1/7	1/5	1	1/2
P7	5	5	1/2	1/2	2	2	1
Consistency		λmax = 7.76, CI = 0.127 CR = 0.096 < 0.1 Reasonably consistent matrix.

and

Table 7. Pairwise Comparison Matrix for the Engineering and Technical Criteria.

Parameter	P1	P2	P3	P4	P5	P6	P7
P1	1	9	1/2	7	7	7	9
P2	1/9	1	1/9	1/7	1/7	1/5	1/2
P3	2	9	1	7	7	7	9
P4	1/7	7	1/7	1	2	2	5
P5	1/7	7	1/7	1/2	1	2	5
P6	1/7	5	1/7	1/2	1/2	1	5
P7	1/9	2	1/9	1/5	1/5	1/5	1
Consistency		λmax = 7.79, CI = 0.132 CR = 0.099 < 0.1 Reasonably consistent matrix.

show these pairwise comparison matrices. All resulting matrices were checked for consistency to address potential decision biases of the evaluators. The analysis under the environmental criteria was highly consistent, offering a homogeneous distribution of weights per parameter. Table 3, Table 4, Table 5, Table 6 and Table 7 also present the pairwise comparison matrices developed for each of the five main criteria in the FAHP analysis: economic, environmental, ecological/management, social, and engineering technical. Each matrix reflects the relative preference of one parameter versus another in terms of importance. These judgments were obtained from expert consultations using the Saaty scale, where values greater than 1 indicate that the row parameter is preferred over the column parameter, while values less than 1 indicate the opposite.

Table 3, which represents the economic criterion, shows that parameters such as proximity to roads (P1) and urban areas (P3) consistently receive higher preference scores, reflecting their direct influence on transportation costs and facility access. The consistency ratio (CR = 0.095) remains below the acceptable threshold of 0.1, indicating reliable pairwise judgments. Table 4, for the environmental criterion, highlights a stronger emphasis on avoiding urban areas (P3) and coastlines (P4), with extremely high comparative values reflecting environmental protection priorities. The consistency ratio (0.035) confirms acceptable logical consistency. Table 5 and Table 6 follow similar logic, evaluating factors such as proximity to wetlands, protected areas, or population centers. Table 7, which addresses engineering and technical feasibility, assigns high weights to accessibility and road proximity (P1, P3) as well as moderate scores for slope-related and infrastructural considerations. The CR in this case is also acceptable (0.099), which validates the consistency of judgments provided.

Across all matrices, the maximum eigenvalue λ_max is close to the number of criteria (n = 7), indicating good consistency between expert inputs. These pairwise comparison matrices form the foundation for deriving the fuzzy geometric means and calculating the weighting factors used in the GIS overlay. As a set, the five tables demonstrate the structured and consistent application of FAHP in determining the relative importance of each parameter across different decision criteria.

The methodology described for the fuzzy geometric mean method in Equations (1)–(7) was used to calculate the weights of each parameter per criterion. The FAHP analysis results are listed in

Table 8. Geometric Means (r_i) of the Parameters.

Parameter	C1 Economic	C2 Environment	C3 Ecological	C4 Social	C5 Engineering
P1	(3.704, 4.278, 4.804)	(0.178, 0.195, 0.235)	(0.282, 0.361, 0.469)	(0.283, 0.361, 0.462)	(3.337, 3.907, 4.568)
P2	(0.224, 0.258, 0.336)	(0.208, 0.23, 0.265)	(0.184, 0.21, 0.255)	(0.195, 0.22, 0.271)	(0.195, 0.22, 0.271)
P3	(3.85, 4.715, 5.304)	(1.131, 1.537, 2.192)	(2.119, 3.061, 3.81)	(2.119, 3.016, 3.747)	(3.904, 4.762, 5.344)
P4	(0.39, 0.534, 0.756)	(1.131, 1.392, 1.873)	(0.783, 0.981, 1.199)	(2.535, 3.212, 3.97)	(0.869, 1.162, 1.426)
P5	(0.589, 0.701, 0.802)	(1.777, 2.246, 2.564)	(1.662, 2.119, 2.661)	(0.906, 1.14, 1.486)	(0.743, 0.953, 1.219)
P6	(0.589, 0.701, 0.802)	(1.777, 2.246, 2.564)	(1.662, 2.119, 2.661)	(0.575, 0.72, 0.944)	(0.599, 0.745, 1)
P7	(0.651, 0.732, 0.85)	(1.811, 2.068, 2.192)	(0.689, 0.981, 1.575)	(1.086, 1.584, 2.284)	(0.248, 0.296, 0.357)

,

Table 9. Fuzzy Weights (w_i) of the Parameters.

Parameter	C1 Economic	C2 Environment	C3 Ecological	C4 Social	C5 Engineering
P1	(0.271, 0.359, 0.481)	(0.015, 0.02, 0.029)	(0.022, 0.037, 0.064)	(0.021, 0.035, 0.06)	(0.235, 0.324, 0.462)
P2	(0.016, 0.022, 0.034)	(0.018, 0.023, 0.033)	(0.015, 0.021, 0.035)	(0.015, 0.021, 0.035)	(0.014, 0.018, 0.027)
P3	(0.282, 0.396, 0.531)	(0.095, 0.155, 0.273)	(0.168, 0.311, 0.516)	(0.161, 0.294, 0.487)	(0.275, 0.395, 0.54)
P4	(0.029, 0.045, 0.076)	(0.095, 0.14, 0.234)	(0.062, 0.1, 0.162)	(0.193, 0.313, 0.516)	(0.061, 0.096, 0.144)
P5	(0.043, 0.059, 0.08)	(0.15, 0.227, 0.32)	(0.132, 0.216, 0.361)	(0.069, 0.111, 0.193)	(0.052, 0.079, 0.123)
P6	(0.043, 0.059, 0.08)	(0.15, 0.227, 0.32)	(0.132, 0.216, 0.361)	(0.044, 0.07, 0.123)	(0.042, 0.062, 0.101)
P7	(0.048, 0.061, 0.085)	(0.152, 0.209, 0.273)	(0.055, 0.1, 0.213)	(0.082, 0.154, 0.297)	(0.017, 0.025, 0.036)

and

Table 10. Global FAHP Results for Site Selection Criteria.

Weights
Parameter	Economic	Environmental	Ecological and Management	Social	Engineering and Technical	Aggregated Results
Global Criteria	8%	3%	33%	16%	40%
Roads	35.85%	2.03%	3.72%	3.54%	32.61%	18%
Airports	2.32%	2.34%	2.14%	2.17%	1.90%	2%
Urban areas	39.00%	16.59%	30.20%	28.59%	38.67%	34%
Coast	4.81%	14.87%	9.84%	31.01%	9.64%	13%
Wetlands	5.88%	22.04%	21.47%	11.32%	8.13%	13%
Waterways	5.88%	22.04%	21.47%	7.18%	6.55%	12%
Protected areas	6.27%	20.10%	11.16%	16.20%	2.49%	8%

and Figure 2 and Figure 3. The defuzzified weight converts the triangular fuzzy weight to a crisp value. However, because the summation of all defuzzified weights per analysis may exceed 100%, the normalized weight is calculated according to Equation (4) to ensure a 100% total (see Figure 2 and Figure 3).

The distance to metropolitan areas (P3) and roads (P1) has considerably higher weights than the other factors when the economic criterion is taken into consideration. This is because of their direct contribution to the building costs of establishing the facility for the dumping of SW and the network that is associated with it. The greater weights of these elements in the environmental criterion reflect the likelihood that streams, wetlands, and protected areas have been contaminated. It is also noteworthy that the distance to urban regions is the factor that carries the most weight among all the other criteria. This exemplifies the difficulties that may arise when making decisions that take into consideration the effects that a systems-thinking approach will have in the long run.

We calculated the aggregated results of the parametric weights presented in Table 10 by incorporating the global weight values determined at the first level. Specifically, the final weight of each parameter was obtained by multiplying its local weight, assigned at the second level, by the corresponding global weight of its parent category at the first level. This hierarchical weighting approach ensures that the influence of each parameter accurately reflects both its individual significance and its contribution within the broader category.

The evaluation of various spatial and non-spatial criteria in this study has revealed significant variations in how different factors influence the suitability of locations for SW landfill development in the Tabuk region. Among all assessed parameters, distance from urban areas proved to be the most critical in determining suitability. This outcome consists of both practical and environmental expectations. Landfills located close to residential communities present substantial risks, including groundwater contamination from leachate, the emission of unpleasant odors, increased waste-related traffic, and adverse public health impacts. Such proximity often leads to community resistance due to aesthetic, health, and safety concerns.

From a planning perspective, ensuring a sufficient buffer between residential zones and waste disposal facilities is essential to avoid long-term conflicts and to preserve urban quality of life. The distance from main roads closely followed in importance. While placing a landfill near populated areas is discouraged, having it reasonably close to transportation infrastructure is a practical necessity. Solid waste must be transported daily in large quantities, and routing it through long, inaccessible paths lead to increased fuel consumption, higher operational costs, and logistical inefficiencies. Accessibility to well-developed road networks supports efficient waste transport, facilitates monitoring, and reduces the time and cost associated with the delivery of materials and equipment. However, proximity must be managed carefully to avoid excessive disturbance from heavy vehicle movement and to minimize any interference with regular traffic flows.

Environmental features such as coastlines, wetlands, and rivers were also found to have a moderate impact on site suitability. These natural systems are ecologically sensitive and serve crucial roles in sustaining biodiversity and hydrological balance. Even though the Tabuk region has an arid climate, the presence of surface water features remains vital for local ecosystems. Landfill development near these features poses a high risk of environmental degradation. Leachate and other contaminants generated by waste decomposition can migrate into water bodies, harming aquatic habitats and rendering water sources unsafe for human or agricultural use. For this reason, maintaining an appropriate distance from such features is considered an important protective measure in sustainable landfill planning.

On the other hand, criteria such as proximity to protected areas and airports showed the least influence on the decision-making process. The relatively sparse distribution of conservation zones and flight paths in Tabuk reduces the likelihood of conflict between these zones and landfill development. Protected ecological reserves are mostly located far from areas under consideration for waste disposal. Similarly, there are only a few airports across the region, and these are generally situated away from potential landfill sites.

While the criteria discussed above are essential in other geographic contexts, particularly in dense urban areas or near international wildlife corridors, their influence is context-specific and therefore limited in the present case. An important aspect of interpreting these results lies in understanding the contextual relevance of each parameter. The Tabuk region is characterized by large expanses of undeveloped land, relatively low population density outside of urban centers, and a growing need for infrastructure development aligned with regional projects like NEOM. These characteristics influence both the selection of criteria and the weights assigned to them. For instance, in regions with highly populated urban belts, the weight of residential proximity might be even higher. In Tabuk, the availability of vast open spaces allows for flexibility in avoiding both population centers and environmentally sensitive zones.

To account for the uncertainty and subjectivity associated with these decisions, a fuzzy logic-based evaluation model was integrated into the analysis. Rather than treating site suitability as a binary variable, the fuzzy approach classifies spatial variables into degrees of suitability. Each location is assessed not just as suitable or unsuitable but along a gradient that reflects how well it meets the desired criteria. The fuzzy classification system uses linguistic categories, e.g., very low, low, moderate, high, and very high suitability, which are converted into a numerical scale from one to five. This method enables a more nuanced and realistic representation of complex decision scenarios, particularly in regions like Tabuk, where trade-offs between environmental and economic priorities must be carefully balanced. For example, while locations close to cities are environmentally undesirable, they are economically favorable due to reduced transportation costs. Similarly, sites near wetlands or rivers may be logistically accessible but environmentally risky. The fuzzy classification captures these nuances, allowing for flexible and adaptive evaluation based on the relative importance of each factor.

In this study, the classified layers were prepared using spatial analysis tools in QGIS and then combined through raster overlay functions weighted by FAHP. Proximity analysis was performed on spatial layers representing each of the major criteria, i.e., distance from urban areas, main roads, coastlines, rivers, wetlands, airports, and protected natural zones. Each proximity map was standardized through reclassification into defined buffer zones. These zones were then assigned suitability values based on expert input and fuzzy logic functions. For example, areas within a short distance from residential zones received a low suitability score, whereas those far removed were scored more favorably. In contrast, proximity to major roads was treated in the opposite way: areas closer to roads were scored higher due to ease of access and reduced operational costs.

The integration of these layers into a single composite map was achieved using raster calculation tools, where each parameter was weighted according to its importance in the FAHP framework. This allowed for the simultaneous analysis of multiple criteria and provided a spatial visualization of the suitability of different zones within the region. The resulting landfill suitability map offers a valuable tool for municipal planners and decision-makers, presenting a clear picture of which areas are most appropriate for landfill development. The overall aim is to balance environmental safety with economic feasibility, technical access, and public acceptability (

Table 11. Categories of FAHP-GIS Memberships.

Parameter	Poor (1)	Moderately Preferred (2)	Strongly Preferred (3)	Very Strongly Preferred (4)	Extremely Preferred (5)
Distance to main road network	0–500 m	>5000 m	4000–5000 m	3000–4000 m	>3000 m
Distance from airports	0–1250 m	1250–2500 m	2500–3750 m	3750–5000 m	>5000 m
Distance from urban areas	0–1000 m	1000–3000 m	3000–5000 m	5000–10,000 m	>10,000 m
Distance from coastline	0–1250 m	1250–2500 m	2500–3750 m	3750–5000 m	>5000 m
Distance from wetlands	0–75 m	75–150 m	150–225 m	225–300 m	>300 m
Distance from waterways	0–75 m	75–150 m	150–225 m	225–300 m	>300 m
Distance from protected areas	0–125 m	125–250 m	250–375 m	375–500 m	>500 m

).

3.2. GIS Analysis

The spatial analysis carried out in this work was supported by various publicly accessible geospatial datasets that were imported and processed using QGIS software. These datasets included high-resolution satellite images, land use classifications, elevation models, and other essential topographic and hydrological features relevant to the Tabuk region. Each dataset was examined for consistency and precision to ensure accurate spatial alignment with the characteristics of the study area. After validation, the data were transformed into formats suitable for spatial modeling, allowing their integration into the FAHP-GIS framework used to identify appropriate landfill locations.

To enhance the reliability of the spatial foundation, information from different sources was compared to verify geographical accuracy and reduce any potential mismatch between datasets. This verification step played a critical role in building a dependable base for the multi-criteria evaluation process. Spatial distance calculations were performed for each relevant feature, including residential zones, road networks, watercourses, protected environments, and sensitive ecological areas. These calculations produced a series of proximity maps that indicated the distance from each point in the region to the corresponding features.

Furthermore, to facilitate interpretation, all proximity maps were generated using a uniform color scale. The measurements were expressed in meters, and a standardized color gradient was applied to each layer. In this format, red areas indicated closer proximity to the analyzed features, while areas shaded in blue represented locations further away. These visual representations were then used to classify locations according to levels of suitability, providing essential input for the fuzzy membership analysis. The spatial information obtained from this step was instrumental in forming the foundation for the landfill suitability model, helping planners and decision-makers assess and compare potential sites with greater confidence and clarity.

Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 illustrate the spatial distribution of various geographical and environmental constraints relevant to landfill site selection. Figure 4 highlights proximity to the main road network, which ensures ease of transportation and accessibility. Figure 5 shows the location of airports, essential for avoiding potential hazards or restrictions related to air traffic. Figure 6 presents the proximity to urban areas and other land uses, ensuring that selected sites do not conflict with residential or sensitive zones. Figure 7 outlines the distance to the coastline, avoiding ecological risks and preserving valuable coastal ecosystems. Figure 8 and Figure 9 display proximity to waterways and wetlands, and to protected areas, respectively, which are critical factors in safeguarding biodiversity and preventing contamination of water resources.

A classification system was developed based on the processed geospatial data and criteria weighing results. The primary purpose of the system was to assess the level of suitability for potential landfill locations across the study area. Five levels of suitability were defined to reflect the varying degrees to which different areas meet the required conditions for hosting a SW disposal site. This classification system was informed by spatial characteristics associated with each criterion and allowed for a structured evaluation of potential sites using a numerical scale.

In the developed system, different values were assigned to different areas on a scale from one to five: A value of one indicated areas that were unsuitable for landfill development, while a value of five represented areas that were most favorable for this purpose. The five-level suitability classification was applied to each geospatial parameter independently, as detailed below.

For the road network, a balanced assessment was necessary to ensure that the site would be accessible though not overly close to key traffic corridors, as close proximity might lead to congestion or public complaints. The spatial analysis showed that a significant portion of the region falls within the moderate preference category, where roads are accessible but not directly adjacent to the potential landfill zones. These areas offer logistical advantages without the environmental drawbacks that could arise from excessive proximity.

The evaluation of airport locations produced a distinctive suitability distribution. Because the few airports in the Tabuk region are generally isolated from residential and industrial zones, the surrounding areas were classified as highly suitable for landfill development. This is primarily due to the reduced risk of direct environmental impact or public health concern, provided that the landfill does not interfere with flight paths or safety regulations.

When analyzing urban proximity, areas that are moderately close to population centers were identified as more favorable. While landfills must not be placed too near to residential zones, locating them within a manageable distance ensures cost-effective transport and operational efficiency. Therefore, regions situated outside city boundaries but not too remote received higher suitability rankings.

The analysis of coastlines, rivers, and wetlands required strict environmental consideration. Narrow zones bordering these features were excluded from high suitability categories due to their ecological sensitivity. These areas were recognized as vulnerable to pollution and disturbance from landfill operations. Thus, only zones that maintained sufficient distance from these natural resources were classified as suitable (Figure 10). This helped to preserve the integrity of aquatic and wetland ecosystems and maintain the quality of surface and groundwater.

Protected areas were also a key parameter in determining site acceptability. The classification model for these lands divided the study area into two sharply contrasting categories. Regions located within or near protected zones were identified as entirely unsuitable, while those outside these zones were considered highly favorable for landfill siting. This clear delineation supports regulatory compliance and ensures that conservation zones remain undisturbed by waste management activities.

Through the application of this five-level suitability classification, a comprehensive understanding of the spatial variation in landfill potential across Tabuk was established. The resulting maps provide a solid foundation for informed decision-making, enabling the selection of landfill locations that are both environmentally responsible and operationally efficient.

To identify optimal sites for SW landfills in the Tabuk region, a structured integration of spatial and decision-making tools was implemented. The classification values derived from FAHP were systematically applied to the geospatial data layers using several GIS operations. These included layer overlay, rasterization, and spatial clipping to ensure consistency in data structure and resolution across all thematic layers. Each individual layer represents key parameters, such as land use, proximity to water bodies, infrastructure, and ecological sensitivity. These were then reclassified into distinct suitability classes based on the fuzzy membership values. The reclassification process allowed for a consistent scale of comparison and aligned the spatial data with the defined decision-making framework.

To produce a comprehensive suitability map, the weighted criteria obtained from the FAHP analysis were mathematically combined with the reclassified raster layers through spatial modeling tools within the QGIS environment. Using the raster calculator module, each pixel in the study area was assigned a composite score by multiplying the fuzzy membership value of each criterion with its corresponding FAHP weight. The sum of these weighted values generated a cumulative suitability index for every location in the region. This process enabled the integration of expert judgment and spatial variation into a single decision-support output. The input layers used in this spatial model included critical factors such as the distance to transportation routes like roads and airports, proximity to sensitive features such as wetlands and rivers, the distance from the Red Sea coast, and prevailing land use types. These inputs reflected both the environmental and logistical considerations required for responsible and cost-effective landfill sitting.

Once the final index map was generated, it was reclassified into five suitability zones: poor, low, moderate, high, and extremely preferred. This final classification transformed complex spatial information into a simple visual decision-making tool that could be interpreted by planners and policymakers. The results of the analysis revealed that the existing landfill sites in the Tabuk region are currently located in areas that fall within the highly suitable zones as defined by the model. This confirms that past selection decisions were mostly aligned with the environmental and planning principles used in the current methodology.

At the same time, the analysis highlighted significant spatial gaps in the availability of SW infrastructure. These gaps were particularly evident in the southern and eastern zones of the region, where rapid population growth and urban expansion are projected to require new landfill facilities to meet future waste management demands. The generated suitability map thus serves not only as a planning tool but also as a foundation for future expansion strategies. By guiding decision-makers toward locations that offer optimal balance between technical feasibility, environmental protection, and public acceptance, the FAHP-GIS model provides a transparent and adaptive approach to SWM land use planning.

The spatial suitability assessment revealed that a significant portion of the Tabuk region exhibits conditions ranging from suitable to highly suitable for the development of SW landfill facilities. The integrated analysis, which combined geospatial layers with expert-derived weights, effectively excluded areas with environmental constraints. These excluded zones accounted for nearly half of the study area and mainly consisted of conservation areas and ecologically sensitive land. The remaining landscape was evaluated in terms of accessibility, environmental safety, and compliance with land use priorities.

The highest-ranked zones for landfill siting were observed in the northwest and southwest portions of the region. These areas displayed a favorable combination of distance from urban developments, minimal ecological impact, and convenient access to transportation routes. To ensure alignment with environmental standards and public health regulations, minimum separation buffers were applied. Specifically, recommended landfill locations were positioned at least 500 m from residential areas, 1250 m from the coastline, and 125 m from designated protected zones. These buffer zones provided an added layer of environmental and social protection while still maintaining the logistical practicality of the selected sites.

The outcome of this spatial modeling was visualized in a comprehensive suitability map, which not only illustrated spatial patterns of optimality but also quantified the extent of each suitability class. The numerical breakdown of land classification categories, presented in tabular form, highlighted that individual areas comprising approximately 31% of the entire region could be considered as potential candidates for landfill development. This targeted identification significantly narrows the focus for decision-makers, enabling more efficient planning and resource allocation. By filtering out unsuitable areas and prioritizing locations with a balanced set of characteristics, the study supports informed, evidence-based infrastructure planning for SWM in the Tabuk region (

Table 12. Percentage of Suitability Memberships in the Tabuk Region.

Suitability	Percentage of Area
Poor	0%
Moderately preferred	0.01%
Strongly preferred	2.74%
Very strongly preferred	66.44%
Extremely preferred	30.82%

).

This study employed an integrated FAHP-GIS approach to identify optimal locations for establishing SW landfills across Tabuk Province. Given the region’s unique environmental characteristics and ongoing urban expansion, landfill site selection must be handled with precision and foresight. The process of determining suitable locations involves multiple layers of criteria that must be assessed together to reduce environmental risks while maintaining economic feasibility and social acceptability.

To address this complex challenge, seven key spatial parameters were selected based on their relevance to landfill planning. The spatial parameters involved distance from road networks, airports, urban areas, coastal zones, water bodies, wetlands, and protected zones. These seven factors were categorized under five broader decision themes, namely technical, environmental, economic, management, and social. The inclusion of both human and ecological parameters ensures a balanced framework for sustainable site selection.

The GIS platform was employed to acquire, process, and manage spatial datasets for each of the seven spatial parameters. Using standardized projections and raster resolution, thematic layers were prepared for analysis, with each layer reclassified according to its contribution to the suitability of a landfill site. For example, areas closer to roads were assigned higher suitability scores due to ease of access and reduced transportation costs, whereas areas closer to wetlands or conservation zones were assigned lower suitability scores due to the danger of environmental degradation.

FAHP assigned relative weights to each parameter through expert judgment under uncertainty. The strength of this method lies in its ability to capture subjective preferences and transform them into a quantifiable scale. These weights were then applied to the corresponding GIS layers through spatial overlay and raster calculations. Each pixel in the study area received a composite suitability score by multiplying its fuzzy membership value by the weight of its respective criterion. The result was a detailed suitability map showing areas of poor, moderate, high, and extremely high potential for landfill development.

Notably, regions in the northwest and southwest of Tabuk emerged as highly suitable, benefiting from favorable distances from sensitive zones while maintaining accessibility. Conversely, zones adjacent to urban areas, wetlands, and nature reserves were categorized as unsuitable due to potential environmental and social impacts. Analysis of the suitability distribution also revealed that nearly one-third of the Tabuk region qualifies as appropriate for landfill establishment, greatly narrowing down the areas requiring in-depth technical assessments. This significantly enhances the planning process by focusing resources and evaluations on zones that already meet minimum spatial and environmental thresholds.

From the above, it is clear that integrating FAHP with GIS streamlines the decision-making process while enhancing transparency and adaptability. The developed model equips local authorities and planners with a robust, data-driven foundation for landfill site selection, ensuring that new facilities are strategically located to minimize environmental harm and maximize functionality. In the case of Tabuk, the approach successfully identified highly suitable areas for landfill development while demonstrating the value of explicitly accounting for uncertainty in solid waste management within arid regions. Nevertheless, some limitations remain, particularly the dependence on available spatial datasets and expert judgments, which may evolve as new data and changing environmental conditions emerge. Future research could strengthen the framework by incorporating additional criteria, such as socio-economic impacts, groundwater vulnerability, or long-term climate variability, thereby improving the accuracy of site selection. Moreover, the methodology is adaptable and can be validated across other provinces in Saudi Arabia and comparable semi-arid settings, supporting national strategies for sustainable waste management. By bridging scientific modeling with practical planning needs, this study offers a replicable tool for authorities seeking to balance environmental protection with infrastructure development.

4. Conclusions

This research presented a comprehensive methodology for optimizing landfill site selection in the Tabuk region of Saudi Arabia through the integration of Geographic Information Systems and the Fuzzy Analytic Hierarchy Process. Landfill site selection is inherently complex, requiring the consideration of multiple interrelated factors, including environmental sensitivity, technical feasibility, economic efficiency, and social acceptability. Traditional approaches to landfill planning often fail to fully address these complexities, particularly under conditions of uncertainty, conflicting criteria, and regional constraints. By integrating Fuzzy Analytic Hierarchy Process with Geographic Information Systems, the proposed framework provides a robust decision-support tool capable of handling both quantitative and qualitative data, systematically incorporating expert judgments, and addressing uncertainty in spatial planning. This approach ensures a more reliable, transparent, and scientifically grounded basis for making landfill siting decisions in semi-arid and ecologically sensitive regions like Tabuk.

Seven spatial parameters were identified as critical for evaluating landfill site suitability: proximity to road networks, airports, urban centers, coastline, waterways, wetlands, and protected areas. The Fuzzy Analytic Hierarchy Process was applied to determine the relative importance of these parameters through pairwise comparison matrices, producing criterion weights that reflect expert priorities and subjective assessments. Geographic Information Systems was then used to overlay and analyze the spatial data, generating comprehensive suitability maps that visually identify areas most appropriate for landfill development. The findings indicate that sites at a reasonable distance from urban centers and major waste generation sources are most suitable, providing a balance between minimizing environmental impact and maintaining operational efficiency. Proximity to urban areas emerged as the most influential factor, emphasizing the importance of carefully managing the relationship between waste disposal and community development, as well as maintaining safe distances from residential areas to protect public health.

The study underscores the importance of prioritizing criteria based on expert judgment rather than treating all factors equally. This approach enhances the rationality of the decision-making process while ensuring greater consistency and transparency in evaluations. Active stakeholder engagement is essential to reflect local priorities and preferences, promote social acceptance, and enhance the legitimacy of planning decisions. By integrating Fuzzy Analytic Hierarchy Process and Geographic Information Systems, the methodology provides a replicable and adaptable framework that can be applied to other regions facing similar municipal solid waste management challenges. Future applications can incorporate additional parameters such as groundwater vulnerability, projected population growth, historical land use, and potential climate change impacts to further refine the framework and maintain its relevance under evolving environmental, economic, and policy conditions.

The practical implications of this research are significant for urban planners, municipal authorities, and environmental agencies. The methodology streamlines landfill site selection, improving both transparency and accountability in environmental decision-making. Moreover, it supports evidence-based policy formulation that is informed by spatial data and expert knowledge. The resulting suitability maps enable decision-makers to visualize trade-offs, highlight priority areas, and identify zones for further investigation, thereby facilitating efficient land use planning and minimizing potential conflicts with human settlements and sensitive ecosystems. This comprehensive approach also promotes the integration of landfill planning into broader urban development strategies, supporting sustainable municipal governance.

Importantly, this methodology directly contributes to the achievement of the Sustainable Development Goal of Good Health and Well-Being by reducing exposure to environmental hazards and minimizing the risk of diseases associated with poor waste management. Strategic landfill planning also advances the Sustainable Development Goal of Sustainable Cities and Communities by ensuring that urban expansion and municipal development proceed in an environmentally responsible, socially acceptable, and technically feasible manner. By systematically integrating environmental, social, economic, and technical criteria, the proposed framework aligns landfill development with global sustainability priorities, demonstrating that local interventions in waste management can have broader impacts on achieving international development goals.

The Fuzzy Analytic Hierarchy Process and Geographic Information Systems framework provides a scientifically grounded, flexible, and transferable approach to landfill planning. It allows urban planners and municipal authorities to make informed, transparent, and evidence-based decisions that optimize environmental protection, enhance community well-being, ensure cost-effective use of infrastructure, and support sustainable urban growth. The framework’s ability to accommodate uncertainty, incorporate expert judgments, and integrate multiple spatial and non-spatial criteria makes it particularly valuable for rapidly developing regions like Tabuk, where increased population, industrial expansion, and urbanization place additional pressure on land use and environmental resources.

By adopting such a decision-support system, authorities can minimize ecological risks, avoid conflicts with communities, and protect sensitive ecosystems, thereby promoting long-term resilience and sustainable development. The spatially explicit outputs generated through Geographic Information Systems facilitate the visualization of trade-offs and priorities, enhancing stakeholder engagement, communication, and public participation in urban planning processes. Furthermore, the methodology’s replicability allows it to be adapted for other arid and semi-arid regions, extending its benefits beyond the immediate study area.

This study highlights the critical role of integrated decision-support systems in achieving sustainable municipal solid waste management. It demonstrates that scientific, systematic, and data-driven approaches to landfill site selection can directly contribute to the Sustainable Development Goals of Good Health and Well-Being and Sustainable Cities and Communities. By embedding public health, environmental protection, and urban sustainability considerations into spatial planning, the study provides a robust and adaptable framework for responsible land use management. The methodology ensures that landfill development is environmentally sound, economically viable, and socially acceptable, while supporting long-term regional planning objectives and international sustainability targets. This research reinforces the importance of combining advanced analytical tools with participatory and evidence-based governance to achieve sustainable urban development, protect public health, and enhance societal resilience.