GIS-based Multi-criteria Decision Research Articles

GIS-based multi-criteria decision analysis for mapping flood-prone areas in Dehradun city, India

GIS-based multi-criteria decision analysis for mapping flood-prone areas in Dehradun city, India

Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria decision analysis method in Fetam-Yisir catchment, Blue Nile Basin, Ethiopia

Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria decision analysis method in Fetam-Yisir catchment, Blue Nile Basin, Ethiopia

Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP).

Nagaon is one of the highly flood-prone districts of Assam, India that recurrently experiences devastatingfloods resulting in the loss of lives and property and wreaking havoc on the district's socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi-criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district's total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood-affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro-spatial scales.

Agricultural land suitability assessment with GIS-based multi-criteria decision analysis and geostatistical approach in semi-arid regions

For sustainable land use planning, evaluating land characteristics and making suitable land use decisions is a priority and critical step. In order to make these evaluations safely, spatial analyzes of many criteria should be made. In this study, the suitability of the land for wheat production was evaluated by Geographical Information Systems (GIS) based Multiple Criteria Decision Analysis (MCDA) in semi-arid conditions. In obtaining the land suitability map; fuzzy set model, Analytical Hierarchy Process (AHP) and GIS are integrated. Ecological criteria weights for agricultural land suitability were determined by AHP. In the suitability analysis, a total of criteria including soil and topographic features were evaluated. Geostatistical analysis approach was applied to determine the spatial variability of soil properties (sand, clay, silt, pH, OM, CEC, ESP, CaCO3, EC). The lowest variation among soil properties was observed in pH (3.8%), while the largest variation was observed in ESP content (107.5%). The nugget/sill ratio is poor for EC and pH, while other soil properties are moderately spatially dependent. According to the results of the analysis, 25.7% (3.226 km2) of the area is highly suitable, while 27.6% (3.457 km2) is moderately suitable and 19.5% (2.440 km2) is marginally suitable for wheat cultivation. In addition, 27.2% (3.415 km2) of the area is not suitable for agricultural production. The use of geostatistical modeling, MCDA and GIS together is very beneficial in making agricultural land management decisions.

Flood susceptibility mapping of Kathmandu metropolitan city using GIS-based multi-criteria decision analysis

Floods are among the most severe hazards around the world triggered by climate change, threatening environmental sustainability, economies, and ecological cycles and damaging infrastructure and causing loss of lives. We aimed to prepare a flood susceptibility map of Kathmandu metropolitan city (KMC) and identify the most flood-susceptible local administrative unit using a multi-criteria decision analysis model, including the analytical hierarchical process and weighted linear combination in geographic information system. Six flood hazard criteria—slope, elevation, drainage density, rainfall, land use and cover, and distance from river—were proposed to produce a flood susceptibility map. Among them, distance from river was the most significant criterion followed by rainfall and land use and land cover. The results showed that a large proportion (56%) of KMC is in moderate flood potential zone, while 32% and 11% proportions of KMC are in high and low flood potential zones, respectively. Wards 4 and 14 of KMC are the highest and lowest flood prone zones, respectively. The accuracy of the obtained map by multicriteria decision model is 81.2%, which was validated through observation of the significant flood events and flood location. These findings are useful for flood risk mitigation and logical allocation of programme budget to the local units. The procedures applied in this study are helpful for hydrologists, urban planners, policymakers, and professionals in hydrology.

Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis

Flood resilience assessment is an important step for any community as it gives the actual scenario of its ability to resist and recover from flood disasters. However, operationalising and quantifying resilience is still a challenge. In Pakistan, very limited research has been done to assess community resilience to floods. The present study proposed an integrated flood resilience model called the “capacity-based flood resilience model (CapFlooR-M)”, which is based on, machine learning (ML), geographical information system (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). The CapFlooR-M incorporates four main components—flood hazard susceptibility (Is), coping capacity (Cc), adaptive capacity (Ac), and transformative capacity (Tc). Random Forest (RF) and Support Vector Machine (SVM) models were used to create a flood susceptibility map, and the AHP was used to compute the relative scores of core capacities, such as Cc,Ac, and Tc and their respective maps were generated. Finally, the susceptibility map was integrated with Cc,Ac, and Tc maps via overlay analysis in GIS to develop a flood resilience map. The overall results reveal that the northwestern and southwestern parts (36.64%; 505 km2) of the study area have moderate to very high resilience, while the central and southeastern parts (63.46%; 877 km2) have very low to low resilience. The findings of this novel approach can support policymakers, land use planners, and other relevant stakeholders to build resilience against flood hazards.

GIS-based multicriteria decision analysis for spatial planning of solar photovoltaic power plants in Çanakkale province, Turkey

A comprehensive site selection analysis is essential for implementing cost-effective and efficient SPPs. We selected optimal sites for SPPs by considering different criteria and restrictions using the GIS-based MCDA. Highly suitable and most suitable sites were determined by GIS and AHP for establishing PV SPP in the Çanakkale province. Furthermore, the potential of these sites to meet the energy demand was examined. Next, the potential power capacity and payback period of each of the five alternative SPP project sites selected from the most suitable sites were analysed. The results demonstrated that 96% of the study area was restricted, 3‰ was most suitable and 3% was highly suitable for PV SPPs. The most suitable sites exhibited the potential to meet all electrical energy needs of the province of Çanakkale during installation. The potential power capacities of the five selected alternatives were between approximately 0.9 and 1.6 MWp, whereas potential payback periods were between 12.1 and 14.7 years.

A GIS-BASED SUSTAINABILITY AND HIGHEST-BEST USE (SHBU) FOR FELDA LANDS DEVELOPMENT PLANNING DECISION-MAKING

FELDA is enforcing a new direction of sustainable FELDA development in the future. It has timely developed a Sustainability and Highest and Best-use (SHBU) framework based on a GIS-based Multicriteria Decision Analysis (MCDA) approach for optimising FELDA lands, particularly crops, and other possible developments. This paper attempts to demonstrate the application of the GIS-based SHBU for FELDA Bukit Rokan discovering its capabilities. It includes generating criterion maps and sub-criterion maps, weighting criterion maps, and creating outcomes – composite maps of cropland suitability and future-physical development. The geospatial analyses for a Bukit Rokan confirmed that the SHBU model could provide the needed information for crop and settlement area developments. The production of the composite crop and physical land development maps significantly helps FELDA management or crop plantation planners optimise lands for crop plantation and future physical development. Hence, its application provides valuable information about the areas and support attributes enhancing FELDA lands development planning decision-making.

Settlement suitability analysis of a riverine floodplain in the perspective of GIS-based multicriteria decision analysis

Riverine floodplains are highly dynamic and the most vulnerable space on Earth particularly in flat alluvial plains of major river systems. Suitable site selection for sustainable human settlements in active floodplain areas is a critical task for decision-makers in terms of quality of lithology, ecologically fragile landmass, climate-induced hazards, immense population pressure, and environmental conservation issues. This research introduces a methodology for settlement suitability zone (SSZ) that employs GIS-based multi-criteria decision-making (MCDM) techniques. As a case study, an altered hydrological regime of the lower Ganga riparian zone was chosen to identify the SSZ as these areas are the most susceptible to riverine hazards. Twelve significant variables reflecting on topography, climate, landscape, and environment have been selected in the multi-criteria evaluation platform. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method is adopted to specify the weights of the criteria and utilize an inverse distance-weighted (IDW) spatial interpolation technique to generate an SSZ map in a GIS environment. The study zone is spatially quantified into five categories, from unsuitable to high-suitable with a natural breaks (Jenks) classification method. Subsequently, the final results are validated through a receiver operating characteristics (ROC) curve using randomly selected 56 hazard-exposed location points. The outcome revealed that 8.45% of the riparian area falls under unsuitable, 21.87% under low-suitable, and 33.27% under moderate-suitable for locating human settlements. The generally suitable and high-suitable categories account for 36.40% of the total study zone. A spatial sensitivity analysis is also applied to gauge the influence of each parameter on the MCDM outcomes. The SSZ mapping results from this study can help local authorities to plan for sustainable settlement development in environmentally fragile areas.

GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia

Floods are the most common and expensive natural calamity, affecting every country. Flooding in the Shebelle River Basin (SRB) in southern Somalia has posed a significant challenge to sustainable development. The main goal of this study was to analyze flood hazard, vulnerability and risk in the part of SRB using GIS-based Multi-Criteria Decision Analysis (MCDA). The flood hazard map was constructed using seven important causative factors: elevation, slope, drainage density, distance to river, rainfall, soil and geology. The results demonstrate that very low, low, moderate, high, and very high flood hazard zones correspond to 10.92%, 24.97%, 29.13%, 21.93% and 13.04% of the area of SRB, respectively. The flood vulnerability map was created using five spatial layers: land use/land cover, population density, distance to road, Global man-made impervious surface (GMIS), and Human built-up area settlement extent (HBASE). In addition, the results of the flood susceptibility and vulnerability maps were used to create a flood risk map. The results demonstrate that for the Shebelle River Basin, 27.6%, 30.9%, 23.6%, 12.1%, and 5.7% area correspond to very low, low, moderate, high, and very high flood risk zones, respectively. The Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) of the flood hazard model exhibited a good prediction accuracy of 0.781. The majority of the basin is at risk of flooding in the very low, low, and moderate ranges; however, some tiny areas are at risk of flooding in the high and very high ranges. Flood hazard, vulnerability and risk maps should be provided and distributed the authorities responsible for flood protection so that people are aware flood risk locations.

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study.

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

GIS-based multi-criteria decision making under Silica Saturation Index (SSI) for selecting the best direct use scenarios for geothermal resources in Central and Southern Rift Valley, Kenya

Direct use (DU) of geothermal energy is site-specific and majorly dictated by economics. Even with the availability of high enthalpy geothermal fluids, DU could still be curtailed by fluid pH, salinity, and silica content. Selecting economically viable best direct use (DU) scenarios for a given geothermal resource needs meticulous matching of geothermal resource characteristics to the nearby economic activities and local infrastructure. Silica saturation index (SSI) analysis should also be factored in to avoid silica scaling of DU facilities and reinjection well. There is a lack of a visual framework for incorporating all the necessary criteria for selecting the best DU scenarios, hence the motivation of this research. The current study employed a GIS-based multi-criteria decision making (MCDM) method: a hybrid of the analytical hierarchy process (AHP) method with the weighted aggregated sum product assessment (WASPAS) method. The model analyzed nine utilization scenarios against eleven criteria to select the best DU scenarios for six high-enthalpy quaternary volcanoes in the central and southern Kenyan rift: Menengai, Elementaita, Eburru, Olkaria, Longonot, and Suswa. The best typical DU schemes identified were horticultural greenhouse warming, spa, and crop drying. The model was validated with existing DU case studies to gauge its ability to predict suitable utilization methods. There was a good matching between the model's selection and existing case studies implicating the synergistic merger of GIS-based MCDM methods and SSI as a robust tool for planning and carrying out the feasibility study for DU of geothermal energy.

Designating Appropriate Areas for Flood Mitigation and Rainwater Harvesting in Arid Region Using a GIS-based Multi-criteria Decision Analysis

Designating Appropriate Areas for Flood Mitigation and Rainwater Harvesting in Arid Region Using a GIS-based Multi-criteria Decision Analysis

GIS-based multi criteria decision analysis techniques used to identify potential groundwater recharge zones in Quetta Valley, Pakistan

GIS-based multi criteria decision analysis techniques used to identify potential groundwater recharge zones in Quetta Valley, Pakistan

Site selection for landfills using GIS-based multi-criteria decision analysis – a case study of National Capital Territory of Delhi, India

Landfill site selection in an urban area like National Capital Territory (NCT) of Delhi is a challenging task due to shortage of suitable land and other environmental, economic and social parameters. This study focusses on the utilisation of geographic information system, open-source libraries and remote sensing for identification of potential landfill sites in Delhi NCT. Multi-criteria decision analysis (MCDA) and other GIS-based approaches are used for analysing various natural parameters derived using remote sensing satellite data products. Resourcesat LISS-III and Sentinel-2 multispectral satellite data is used for deriving the required parametric layers. Zones in the vicinity of monuments, transport, canals, drainages, lineaments are avoided altogether. Results reveal overall, most suitable places for landfill site in study area is around 4.05 sq. km (0.27%), moderately suitable places are 14.68 sq. km (0.98%), least suitable places are 25.46 sq. km (1.71%) and not suitable places is 1,419.11 sq. km (95.62%).

Optimal Solar Farm Site Selection in the George Town Conurbation Using GIS-Based Multi-Criteria Decision Making (MCDM) and NASA POWER Data

Many countries are committed to boosting renewable energy in their national energy mix by 2030 through the support and incentives for solar energy harnessing. However, the observed solar data limitation may result in ineffective decision making, regarding solar farm locations. Therefore, the aim of this study is to utilise GIS-based multi criteria decision making (MCDM) and NASA POWER data to identify the optimal locations for solar farm installations, with the George Town Conurbation as a case study. Although NASA POWER is tailored for the application, at least, on the regional level, the information it provided on the solar radiation and the maximum and minimum temperatures are deemed useful for the initial solar mapping attempt at the local level, especially in the absence or lack of local data. The performance of the GIS-based MCDM model is categorized as good in identifying solar farms. There are no significant differences in the area under the curve (AUC) values between the map of the NASA POWER data and ground-measured data. This indicates the potential of using the NASA POWER data for generating the much-needed initial insights for the local optimal solar farm site selection. The stakeholders can benefit from the suitability map generated to effectively target the locations that have the highest potential to generate solar energy efficiently and sustainably.

Delineation of groundwater potential zones at micro-spatial units of Nagaon district in Assam, India, using GIS-based MCDA and AHP techniques.

Groundwater is one of the most precious naturalresources in the densely populated regions of the world. The ever increasing population in the developing countries like India and the concurrent growth of industrial and urban centres along with modern agricultural systems have caused tremendous pressure on groundwater. In the present day context of groundwater depletion and degradation, it is necessary to identify and map the groundwater potential zones (GWPZs) of an area to manage and utilize them sustainably. The present study is, therefore, an attempt to employ the GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP) techniques to derive the GWPZs in Nagaon district of the middle Brahmaputra valley agro-climatic region of Assam, India. Here, ten multi-influencing groundwater potential factors, including geology, lineament density, geomorphology, slope, soil texture, LULC, rainfall, drainage density, NDWI, and TWI are considered for the delineation ofGWPZs. These criteria are assigned weightages using AHP wherein geology has been given the highest weight, followed by lineament density and the others. Based on these techniques, the GWPZs of the district are classified into four zones, namely, low, moderate, high, and very high which include 20, 301, 218, and 392 villages, respectively. The results are validated with the groundwater levels of 28 tube wells and 5 ring wells distributed throughout the district, and as such, an area under the curve value of 86.9% is found. The results, thus, validated imply that the methodology adopted in deriving the GWPZs is highly reliable, which can help in sustainable utilization, development, and management of groundwater resources in the district. Besides, the identification and mapping of village-wise GWPZs are very essential for the planning of water resource management and water risks mitigation which ultimately help insustainable rural development.

Geo-fence planning for dockless bike-sharing systems: a GIS-based multi-criteria decision analysis framework

The inappropriate parking of free-floating shared bikes is a critical issue that needs to be addressed to realize the potential environmental, socioeconomic, and health benefits of this emerging green mode of transport. To address this challenge, this paper developes a Geographic Information Systems (GIS) based Multi-Criteria Decision Analysis (MCDA) framework for geo-fence planning of dockless bike-sharing systems based on openly accessible data. The Analytic Hierarchy Process (AHP) and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method are applied in the proposed framework to derive optimal geo-fence locations. The proposed framework is validated in a case study using a dataset of dockless bike-sharing trips from February 2020 in the City of Zurich and comparing the selected geo-fence locations with the existing bike-sharing stations. The assessment results show that the calculated geo-fence locations have a smaller average distance of 1395 m than that of 1692 m, and a larger demand coverage of 81% than that of 77% for bike-sharing stations. Overall, the proposed framework and the insights from the case study can help transport planners better implement shared micro-mobility hence facilitating the uptake of this sustainable mode of urban transport.

GIS-based multi-criteria territorial suitability assessment for insect farms: a case study for North Italy

Although environmental sustainability and economic feasibility frameworks have been developed to evaluate the impact of insect farms, significant studies on the development of territorial suitability methods specifically tailored to insect production have not yet been performed. This work proposes a GIS-based multi criteria decision making analysis to evaluate the suitability of a territory to the installation of insect farms. A case study developed specifically for black soldier flyHermetia illucens(HI) insect farms in Emilia-Romagna region (North Italy) is presented. This is an area which, due to its agro-industrial nature and the consequent large production of related waste and by-products, is an optimal candidate for the installation of insect farms. Nine physical, environmental, and economic criteria were assessed. A raster spatial layer for each criterion was obtained, and their values were standardised. The criteria were weighted using the analytical hierarchical process, based on expert opinion recorded via an online questionnaire. The resulting weights were used to combine the single criterion maps using a weighted linear combination procedure and generate, after reclassification, the final suitability map. This map indicates the areas suitable for insect farms at the local level and provides indications for estimating suitability at regional and provincial level. The results showed that 56.2% of the study area was deemed unsuitable, and 43.8% was determined suitable for an HI insect farm; best locations were found around the main agro-industrial centres of the Po valley. The proposed methodology can be easily adapted to other breeding models, insects or study areas and adds valuable information in the development of guidelines for industrial-scale insect farms.

A GIS-based multi-criteria decision analysis approach using AHP for rural settlement site selection and eco-village design in Erzincan, Turkey

In recent years, natural resource depletion, rapid population growth, and increasing urbanization have all reached new heights. All of these ecological problems that exceed the planet's absorbing capacity, have begun to imperil both human life and nature itself. Rural settlement plans aim to optimize the use of natural resources. Agricultural lands are used for non-agricultural purposes as a result of proper placement planning of rural settlements. The suitability of land for a specific purpose is determined via suitability analysis. Therefore, it is extremely important to use technological innovations in the selection of rural settlement areas in addition to considering the technical, economic, and social factors in determining the most suitable locations. With the advancement of computer technology, Geographical Information System (GIS) has become an important tool for more effective use of rural areas. In this study, the most suitable rural settlement area was determined by using GIS in Erzincan Province. Technical, social, cultural, and economic criteria were developed for the selection of the location. Alternative locations were analyzed using the GIS, Multi-Criteria Decision Analysis and the Analytical Hierarchy Process method based on the criteria and site data. The most suitable areas for rural settlements were chosen as the Merkez, Tercan, Kemaliye, and Kemah districts in Erzincan Province. Çağlayan town in the Merkez district has been determined as the most suitable region for an eco-village and a cost calculation and 3D eco-village design were carried out in Çağlayan.