Search Results (159)

This study was conducted using Geographic Information Systems (GISs), Remote Sensing (RS) techniques, and the SLEUTH model based on Cellular Automata (CA) to analyze the spatial and temporal dynamics of urban growth in Sanliurfa Province and to create future projections. The model in question simulates urban sprawl by using Slope, Land Use/Land Cover (LULC), Excluded Areas, urban areas, transportation, and hill shade layers as inputs. In addition, disaster risk areas and public policies that will affect the urbanization of the city were used as input layers. In the study, the spatial pattern of urbanization in Sanliurfa was determined by using Landsat satellite images of six different periods covering the years 1985–2025. The Analytical Hierarchy Process (AHP) method was applied within the scope of Multi-Criteria Decision Analysis (MCDA). Weighting was made for each parameter. Spatial analysis was performed by combining these values with data in raster format. The results show that the SLEUTH model successfully reflects past growth trends when calibrated at different spatial resolutions and can provide reliable predictions for the future. Thus, the proposed model can be used as an effective decision support tool in the evaluation of alternative urbanization scenarios in urban planning. The findings contribute to the sustainability of land management policies. Full article

Water scarcity remains critical in Rwanda’s Eastern Savanna Agroecological Zone due to erratic rainfall, prolonged dry seasons, and rising water demands. This challenge threatens agricultural productivity, food security, and livelihoods. Stormwater harvesting presents a sustainable solution that increases water availability and mitigates the impacts of climate variability. This study utilizes Geographic Information System (GIS) tools and SCS-CN to assess stormwater harvesting potential in the region. The methodology includes analyzing land use, soil type, rainfall data (30 years, from 1994 to 2023), and topography. Key research steps involve delineating catchment areas, estimating runoff volumes, and selecting optimal storage sites using multi-criteria decision analysis. Findings include eight main water reservoirs, each with a unique code (W_R1 to W_R8), geographic coordinates (X and Y), and 10 million cubic meters storage volumes. W_R1 has the smallest volume at 0.242 × 10⁶ m³, while W_R2 has the largest volume at 8.51 × 10⁶ m³. W_R3, W_R5, and W_R7 are additional noteworthy reservoirs with sizable capacities. The findings contribute to policy formulation and Sustainable Development Goals (SDGs) related to clean water, food security, and climate action. This research provides a replicable framework for addressing water scarcity and enhancing long-term resilience in water-stressed regions. Full article

Driven by global climate change and sustainable development, the coordinated development of multiple industries based on photovoltaic energy in the “Desert-Gobi-Wilderness” region has become the key to achieving sustainable development, as well as transforming and upgrading the energy structure. However, the site selection decision for “Photovoltaic +” multi-industry integration, which takes into account economic, social and ecological benefits in a complex ecological environment, is still a key difficulty that restricts the feasibility and scalability of the project. This study first identified and systematically analyzed six “PV +” multi-industry integrations suitable for development in China, including “PV + sand control”, “PV + agriculture”, “PV + agriculture + tourism”, “PV + animal husbandry”, “PV + animal husbandry + tourism”, and “PV + tourism”. Then, a site selection decision framework for “PV +” multi-industry integration consists of three parts. Part 1 establishes a multi-dimensional suitability assessment system that takes into account heterogeneous data from multiple sources. Part 2 uses an integration method based on BWM-CRITIC-TODIM for priority ranking analysis, which first uses a Geographic Information System (GIS) to carry out suitability simulation for the entire region of China—identifying six alternative regions—then uses the interactive and multi-criteria decision-making (MCDM) method to prioritize the alternative areas. Part 3 carries out further sensitivity analyses, scenario analyses, and comparative analyses to verify the dynamics and scientific nature of the site selection decision framework. Finally, this study identifies regions of high suitability for development corresponding to the six multi-industry integrations. The framework is designed to help decision stakeholders achieve precise site selection and benefit optimization for “PV +” multi-industry integration and provides a replicable planning tool for achieving industrial synergy and sustainable development in the “Desert-Gobi-Wilderness” region driven by green energy. Full article

As technological progress and population growth continue to drive rising energy demand, renewable energy has emerged as a key focus of the global energy transition due to its environmental sustainability. However, in suitability assessments and site selection for green energy projects such as photovoltaic (PV) power generation, key criteria such as supply–demand balance and land price are often inadequately considered, despite their direct impact on decision outcomes. Moreover, excessive reliance on expert judgment for weighting, along with the neglect of inter-criterion relationships, introduces uncertainty. Combined with the presence of ill-posed problems, these issues limit the practical value of the evaluation results. This study integrates economic cost–benefit analysis into the evaluation criteria system alongside climatic and geographical criteria, constructing a set of 11 spatial indicators, including global horizontal irradiation (GHI), land prices, and regional power demand, to support PV site selection. Furthermore, a comprehensive evaluation framework is proposed that combines geographic information systems (GIS), multi-criteria decision analysis (MCDA), fuzzy comprehensive evaluation (FCE), and machine learning (ML). The framework enables the collaborative optimization of expert-constrained and data-driven criteria weighting. A national suitability zoning map for PV power plants was developed and validated against actual construction cases. The results demonstrate that the proposed methodology outperforms traditional approaches, achieving a 0.1178 improvement in weight determination compared to expert-based methods, producing a photovoltaic suitability map that more accurately reflects actual construction trends, thereby providing better and more effective support for PV site planning. Full article

Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP). In this study, ten flood causative factors are employed to generate flood-prone zones. AHP, a form of weighted multi-criteria decision analysis, is applied to assess the relative impact weights of these flood causative factors. Subsequently, these factors are integrated into ArcGIS Pro (Version 3.3) to create a flood susceptibility map for the study area using a weighted overlay approach. The resulting flood susceptibility map classified the county into five susceptibility zones: very low (17.48%), low (41.89%), moderate (37.53%), high (2.93%), and very high (0.17%). The FEMA flood hazard map of Davidson County is used to validate the flood susceptibility map created from this approach. Ultimately, this comparison reinforced the accuracy and reliability of the flood susceptibility assessment for the study area using integrated GIS and AHP approach. Full article

This study presents a comprehensive flood-hazard assessment and mapping of the Messapios River catchment in Evia Island, Greece, utilizing a combination of Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GISs). Flood-prone zones were identified based on five critical factors, which were determined to be the most influential in the watercourse when excessive discharge overwhelms the drainage network’s capacity: slope, elevation, proximity to stream channels, geological formations, and land cover. The Analytic Hierarchy Process (AHP) was applied to assign weights to these factors, while the final flood-hazard map was generated using the Weighted Linear Combination (WLC) method. The analysis revealed that 17.8% of the catchment, approximately 39 km², falls within a very high flood-hazard zone, while 18.02% (38.91 km²) is classified as highly susceptible to flooding. The flood-prone areas are concentrated in the central, southern, and western parts of the study area, particularly at the lower reaches of the catchment, on both sides of the main streams’ channels, and within the gently sloping, low-lying fan delta of the river. The study area has high exposure to flood hazards due to the significant population of approximately 9000 residents living within the flood-prone zones, a fact that contributes to the area’s potential vulnerability. Additionally, critical infrastructure, including five industrial facilities, the Psachna General High School, the local Public Power Corporation substation, about 21 km of the road network, and 21 bridges are located within the zones classified as having high and very high flood-hazard levels. Furthermore, about 35 km² of economically vital agricultural areas (such as parts of the Psachna and Triada plains) are situated in highly and very highly prone to floods zones. MCDA proved to be an effective and reliable approach for assessing and mapping flood-hazard distribution in the Messapios River catchment. The results provide valuable insights to assist decision-makers in prioritizing intervention areas and efficiently allocate resources. Full article

The development of hydropower projects is crucial to addressing Pakistan’s ongoing energy and financial crises. Despite the country’s abundant hydropower resources, particularly in the northern regions, these have not been adequately explored, while energy consumption and supply issues have persisted for the past two decades. Focusing on Sustainable Development Goal (SDG-7): “Ensure access to affordable, reliable, sustainable, and modern energy”, this study aimed to assess the hydropower potential at suitable sites in the Upper Indus Basin (Pakistan) by integrating Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDM). This study not only focused on estimating hydropower but also considered the environmental constraints at all sites by using the multi-criteria decision-making (MCDM) tool, which used the location and constraint criteria, along with benefit and cost criteria. The methodology combines technical evaluations (head and discharge) with environmental constraints to prioritize sustainable hydropower development. Key findings identify sites 17, 15, 16, 5, and 6 as the most promising locations, balancing energy generation with minimal environmental impact. This study provides a replicable framework for policymakers to harness hydropower resources responsibly, contributing to Pakistan’s energy security and aligning with global Sustainable Development Goals. This approach not only bridges the gap between technical feasibility and environmental sustainability but also offers a model for other regions facing similar energy challenges. Full article

Wind energy is a promising alternative energy source to cover large amounts of electricity demand in African countries. Djibouti’s proximity to the Red Sea and its arid and semi-arid climate generate consistent and robust winds, contributing to its potential for wind energy. Notwithstanding its considerable potential, Djibouti has not been adequately examined in earlier studies to determine suitable sites for wind farms. The objective of this study is to develop a model by integrating CRiteria Importance Through Intercriteria Correlation and Combined Compromise Solution methods into a Geographic Information System-based decision-support system to establish a comprehensive framework for the selection of wind farm sites in Djibouti. Following an in-depth review of the literature, seven main criteria were identified to assess the suitability of potential sites for wind farm construction: wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The CRiteria Importance Through Intercriteria Correlation method objectively determines the relative importance of the criteria, identifying wind speed and proximity to power transmission networks as the most important, and ground slope and land use as less important than the other criteria. The Combined Compromise Solution method is employed to prioritize potential sites for wind farms, considering seven specified criteria. To enhance the reliability of the results derived from the Combined Compromise Solution method, validation was conducted utilizing the Multi-Attribute Ideal–Real Comparative Analysis method. The comparative analysis revealed a robust correlation between the results of the two methods, providing convincing evidence for the accuracy and reliability of the proposed decision-support system employed to determine the most suitable sites for wind farms in Djibouti. This study is expected to assist professionals and researchers in dealing with the wind farm site selection problem on an unprecedented scale and with exact coordinates through a decision-support system that concurrently integrates the most recent multi-criteria decision-making methods and Geographic Information System tools. Full article

Nowadays, modelling groundwater potential zones (GWPZs) based on scientific principles and modern techniques is a major challenge for scientists around the world. This challenge is even greater in arid and semi-arid areas. Unmanned aerial vehicles (UAVs), geographic information systems (GISs), and multi-criteria decision making (MCDM) are modern techniques that have been applied in various fields, especially in groundwater exploration. This study attempts to apply a workflow for modelling the GWPZs using UAV technology, GIS, and MCDM in semi-arid areas. An aerial survey provided a high-resolution DEM of 4 cm. Six influencing factors, including elevation model, drainage density, lineament density, slope, flood zone, and topographic wetness index, were considered to delineate the GWPZs. Four classes of groundwater potential were identified, namely high (4.64%), moderate (23.74%), low (18.2%), and very low (53.42%). Three validation methods, namely borehole yield data, receiver operating characteristic area under the curve (ROC-AUC), and principal component analysis (PCA), were used and gave accuracies of 82.14%, 65.4%, and 72.49%, respectively. These validations indicate a satisfactory accuracy and justify the effectiveness of the approach. The mapping of GWPZs in semi-arid areas is very important for the availability and planning of water resources management and for sustainable development. Full article

This research paper examines how to assess potential locations for wind turbines and photovoltaic modules by combining Geographic Information Systems (GIS) with multi-criteria decision-making (MCDM). These potential locations depend on the current legislation, where many areas are buffer zones due to limitations. The study area is Karpathos, which faces energy and water scarcity. The need to increase the penetration rate of renewable energy sources (RES) by 2030 can help this island to fulfill both its energy and water needs through RES. To apply the weighted linear combination technique, this approach considers all eligibility criteria according to the legislation. After classifying them into four zones, the MCDM results in a suitability map that displays the spatial distribution of the final score, ranging from sites that are not appropriate to areas that are highly suitable. In the photovoltaic module scenario, the buffer zone corresponds to 61% of the island, while in the wind turbine scenario, this number increases to 85%, highlighting the difficulty of finding suitable sites. A sensitivity analysis is performed to determine the impact of the criteria on the suitability of a site for both scenarios. Full article

Urbanization in Jaipur, India, has led to a 42% decline in green cover over the past two decades, exacerbating urban heat, air pollution, groundwater depletion, and reduced livability. Green Infrastructure (GI) offers a sustainable solution, but effective implementation requires robust, data-driven strategies. This study employs geospatial technologies—GIS, remote sensing, and Multi-Criteria Decision Analysis (MCDA)—to develop a suitability model for Urban Green Infrastructure (UGI) planning. Using an entropy-based weighting approach, the model integrates environmental factors, including the Normalized Difference Vegetation Index (NDVI), which fell by 18% between 2000 and 2020; Land Surface Temperature (LST), which increased by 1.8 °C; soil moisture; precipitation; slope; and land use/land cover (LULC). Proximity to water bodies was found to be a critical determinant of suitability, whereas land surface temperature and soil moisture played significant roles in determining UGI feasibility. The results were validated using NDVI trends and comparative analysis with prior studies so as to ensure accuracy and robustness. The suitability analysis reveals that 35% of Jaipur’s urban area, particularly peri-urban regions and river corridors, is highly suitable for UGI interventions, thereby presenting significant opportunities for urban cooling, flood mitigation, and enhanced ecosystem services. These findings align with India’s National Urban Policy Framework (2018) and the UN Sustainable Development Goal 11, supporting climate resilience and sustainable urban development. This geospatial approach provides a scalable methodology for integrating green spaces into urban planning frameworks across rapidly urbanizing cities. Full article

Finding optimal locations for wind farms requires a delicate balance between maximizing energy generation potential and addressing the socio-economic implications for local communities, particularly in regions facing socio-economic challenges. While existing research often focuses on technical and economic aspects of wind farm siting, this study addresses a crucial research gap by integrating sustainable supply-chain-management principles into a comprehensive site-selection framework. We present a novel approach that combines Geographic-Information-System-based spatial analysis, the Fuzzy Analytic Hierarchy Process, and multi-criteria decision-making techniques to identify and prioritize optimal wind farm locations in Tunisia. Our framework considers not only traditional factors, like wind speed, terrain slope, and road and grid infrastructure, but also crucial socio-economic indicators, such as unemployment rates, population density, skilled workforce availability, and land cost. Based on the spatial analysis, it was revealed that 33,138 km² was appropriate for deploying large-scale wind systems, of which 6912 km² (4.39% of the total available area) was categorized as “most suitable”. Considering the SSCM evaluation criteria, despite the minor variations, the ARAS, COPRAS, EDAS, MOORA, VIKOR, and WASPAS techniques showcased that Kasserine, Kebili, and Bizerte stood as ideal locations for hosting large-scale wind systems. These rankings were further validated by the Averaging, Borda, and Copeland methods. By incorporating this framework, the study identifies locations where wind energy development can be a catalyst for economic growth, social upliftment, and improved livelihoods. This holistic approach facilitates informed decision making for policymakers and investors, thus ensuring that wind energy projects contribute to a more sustainable and equitable future for all stakeholders. Full article

Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning and site selection for optimal performance. This study presents a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework utilizing the analytic hierarchy process (AHP) to identify optimal sites for utility-scale photovoltaic (PV) farms in Ikorodu, Lagos State, Nigeria. By integrating critical environmental, technical, economic, and social factors, the model evaluates land suitability for solar energy projects across the study area. The finding indicates that 68.77% of the land is unsuitable for development, with only 17.78% classified as highly suitable and 12.67% as moderately suitable. Marginally suitable and most appropriate areas are minimal, at 0.73% and 0.04%, respectively. This study provides a replicable approach for stakeholders and policymakers aiming to implement sustainable energy solutions, aligning with national renewable energy targets. Future research could integrate dynamic factors such as community engagement, land use changes, and evolving environmental policies to enhance decision-making models. This framework offers valuable insights into renewable energy planning and contributes to advancing Nigeria’s transition to sustainable energy systems. Full article

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays a critical role in ensuring the safety and operational efficiency of facilities. This case study uses a combination of the multi-criteria analysis approach and hydrological studies that use machine learning algorithms to simulate new rainfall events in order to estimate the risk of flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, and land use and land cover, will be weighed using the multicriteria approach. A methodical evaluation of the most vulnerable locations on the railroad network will be possible thanks to the analysis of these parameters based on the geographic information system (GIS) approach. In the meantime, historical precipitation, flow, and hydrological balance data will be used to calibrate and validate hydrological models. The database required for the machine learning model can be created with these hydrological data. The research regions are situated in the densely rail-networked state of Minas Gerais. The geographical and climatic diversity of Minas Gerais makes it the perfect place to test and validate the suggested approaches. The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R² value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011). Full article

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. Full article