Geographic Information System Environment Research Articles

Landslide Prediction in Mountainous Terrain Using Remote Sensing and GIS: A Case Study of Al-Hada Road, Makkah Province, Saudi Arabia

In Saudi Arabia’s mountainous regions, debris flow is a natural hazard that poses a threat to infrastructure and human lives. To assess the potential zones of landslide in the Al-Hada Road area, a study was conducted using Geographic Information System (GIS) analysis and remote sensing (RS) data. The study took into account various factors that could affect landslides, such as drainage density, elevation, slope, precipitation, land use, geology, soil, and aspect. The study also included a field trip to identify 11 previous landslide events that were influenced by high-density drainage and slope. The study utilized weighted overlay analysis in a GIS environment to create a map indicating the potential landslide zones in the area. According to the analysis, low-risk areas cover 35,354,062.5 square meters, medium-risk areas cover 60,646,250 square meters, and high-risk zones cover an area of 8,633,281 square meters. This result was verified using the locations of previous landslide events. The study’s findings can help planners and decision-makers identify and prioritize areas for mitigation and prevention measures. Additionally, the study contributes to understanding landslide hazards in arid and semi-arid regions.

Structural readiness of health facilities in Mozambique: how is Mozambique positioned to deliver nutrition-specific interventions to women and children?

Background The health sector is essential in delivering high-quality nutrition interventions to women and children in low and middle-income countries, and Mozambique is no exception. Still, the quality and readiness of health services to deliver nutritional services have yet to be comprehensively mapped across the country. We assessed the accessibility and readiness of health facilities to deliver maternal and child nutrition services in Mozambique. Methods Using multiple data sources within a geographic information system (GIS) environment, we calculated facility readiness to deliver nutritional services, population access to health facilities, and health facilities ready to deliver nutrition services. Data from Mozambique’s 2018 Service Availability and Readiness Assessment (SARA) was used to calculate readiness scores for each facility in the country. We used geospatial data from the ´WorldPop´ initiative to estimate the proportion of people in Mozambique within 10 kilometers of a ready facility. For each province and the country as a whole, we calculated the proportion of people with access to a ready facility for maternal and child nutrition interventions. Results At the national level, 29.1% and 37.3% of the population were within 10 kilometers of a facility ready to deliver all maternal and child nutrition services, respectively. Pregnancy growth monitoring (73.8%) and vitamin A supplementation (72.4%) were the most available interventions to the population. In contrast, anemia testing/iron supplementation (45.1%) was the least available nutritional intervention. The Center (30.5%) and North (26.9%) regions of Mozambique had much lower coverage than the South region (71.7%) across the maternal and child nutrition interventions. Nampula (14.9%) and Zambézia (17.9%) provinces were the least ready to deliver maternal nutrition services while Nampula (20.4%) and Cabo Delgado (21.2%) provinces were the least ready to deliver nutrition services to children. Conclusions To achieve high coverage of nutrition interventions, facilities need a full suite of commodities and equipment. Currently, too many facilities in Mozambique have only some of these supplies, meaning that only a minority of the children and pregnant women will receive effective nutrition services when they need them. Multi-sectoral efforts, including those outside the health system, are necessary to improve nutrition in Mozambique.

Modelling and mapping Soil Organic Carbon in annual cropland under different farm management systems in the Apulia region of Southern Italy

Soil Organic Carbon (SOC) plays a crucial role in many soil functions and ecosystem services. Monitoring its spatial and temporal changes is essential for planning strategies to minimize soil degradation and loss and maintain its quality. Conservation Agriculture (CA) can make a significant contribution to increasing SOC. This article reports on the spatially modeled SOC concentration in the topsoil (0–0.3 m) of the Annual Cropland (ACL) under Conventional Management (CM) and CA in the Apulia region in Italy. To assess the spatial and temporal dynamics of SOC at the regional scale, the “Scorpan-SSPFe” (soil spatial prediction function with spatially autocorrelated errors) approach to predictive modeling and mapping of soil, based on the Geographically Weighted Regression (GWR) model was performed. The method was implemented using a Geographic Information System (GIS) and Google Earth Engine (GEE) environment to calculate the percentage distribution for each SOC level, altitude, and slope class and their combination. 80 environmental variables and 250 soil samples were analyzed to map the SOC in ACL. The SOC values showed an average of 16.68 and 17.73 g/kg for CM and CA respectively. Adequate map accuracy was obtained by GWR, which showed an R2 of 0.71 for CA and R2 of 0.52 for CM The Root Mean Squared Error (RMSE) predictions obtained were better in CA (3.96 g/kg) than CM (5.65 g/kg) with a percentage RMSE difference of 30 %. Predicted SOC obtained by GWR ranged from 4.06 to 35.60 g/kg for CA and from 5.00 to 29.99 g/kg for CM. The proposed method was shown to be promising in predicting SOC in a region of the Mediterranean area and can be used to assess the effect of land use changes, such as the application of CA, on SOC in the whole basin.

Supporting Informed Public Reactions to Shipping Incidents with Oil Spill Potential: An Innovative Electronic Platform

The analysis of the 2002 Prestige tanker accident showed how public misinformation can worsen marine pollution incidents, rendering their management suboptimal as these evolve, thus raising the issue of appropriately informing and educating coastal and island populations who are at risk. Two decades later, developments in electronic platforms, including Geographic Information Systems (GIS), the Automatic Identification System (AIS) for ship signal transmission, and social media, provide a set of means for public monitoring of such incidents, creating the possibility to antagonise effectively erroneous or malevolent information, which can hinder efficient actions for containing marine pollution risks even without active training of the populations concerned. The authors, in the framework of the development of the Marine Coastal Observatory and Risk Management project “AEGIS+”, have developed E-S.A.V.E., an online innovative platform that (a) meets the needs of different users as revealed by a survey run across groups of them, (b) uses a suitable Geographic Information System (GIS) environment, (c) cooperates with public authorities, for the reliable update of automated systems, and (d) employs an artificial intelligence (AI)-supported tool for social media monitoring; the platform also provides educational resources and information on national and international resources on marine environmental protection and sustainable maritime logistics.

Land Suitability Assessment for Pistachio Cultivation Using GIS and Multi-Criteria Decision-Making: A Case Study of Mardin, Turkey.

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region.

A Geospatial Analysis Model for the Selection of Post-Mining Land Uses in Surface Lignite Mines: Application in the Ptolemais Mines, Greece

Among the procedures included in surface mines’ closure, the determination of post-mining land uses constitutes one of the early but primary steps. This research aims to develop an algorithm for the selection of the most suitable land use spatial distribution in the post-mining area of a surface lignite mine in northern Greece. Considering the already reclaimed areas and the local socioeconomic conditions, six distinct criteria that concern physical local characteristics were selected and, in turn, spatially combined with parameters affecting the mining area. Mining experts attributed weights to the criteria regarding their importance for the examined land uses. The six criteria concerned physical local characteristics (slope, elevation, and distance from villages, rivers, roads, and transmission lines), while the parameters affecting the mining area referred to the type of ground (undisturbed or graded areas), existing infrastructure, and mine closure planning, emphasizing the final landscape of the mining area. The investigated land uses encompassed agricultural, forest, industrial (including buildings, infrastructure, and photovoltaic parks), and recreational parks. Through the application of a fuzzification algorithm within a geographical information system (GIS) environment, four land use suitability maps were generated, which were subsequently overlaid to derive a comprehensive suitability map. The final suitability map was derived from the integration of the mining parameters as spatial information into the algorithm. The findings indicate that, even though the land use suitability analysis could be derived from a mathematical model, the integration of qualitative information related to the mining specifications is necessary to produce more reliable results. The proposed algorithm can be used as a useful tool by decision-makers in the mining industry to plan post-mining reclamation based on suitable criteria.

Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.

Development of Highway Information System in GIS Environment Himachal Pradesh

Managing information to assist more efficient decision making is currently in high demand for transportation organizations, from engineering at the micro level to statewide planning and management at the macro level. Furthermore, data sharing and cooperation among several government agencies at all levels are required to address the complex environmental and economic development issues that face society today. In the past, the majority of transportation agencies and government sectors frequently used information systems and database development for particular applications. The use of geographic information systems (GIS) outside of the transportation sector has been quite effective. Since GIS in transportation (GIS-T) has been used in numerous transportation projects to detect transportation-related problems, it (GIS-T) offers thorough and timely information for sound decision-making. Using geographic information systems for storage is effective. Geographic data from the real world is retrieved, modified, and displayed for transportation systems. In many nations throughout the world, GIS has been utilized as an effective development platform for information systems for the management of pavement maintenance. Road widening or upgrading, maintenance management, evaluation, geometric design and improvement, and other operations related to road transport can all be managed with the help of a GIS-based Highway Information System (HIS). In the current dissertation, GIS was used to create a highway information system. Many road factors including topography characteristics, distresses, traffic data, highway amenities, and pavement data were gathered.

Site suitability analysis for locating construction and demolition waste transfer station: an Indian case study.

Construction and Demolition Waste Management (CDWM) includes collecting, transporting, processing, and disposing construction and demolition (C&D) waste, where collection and transportation of bulky and voluminous C&D waste contribute significantly to economic and environmental impacts. Transfer station (TS) being a link between various waste management (WM) facilities plays a significant role in collection and transportation of waste. Thus, locating TS at suitable site can help in reducing the overall impacts. Employment of Geographic Information System (GIS) analysis tools in CDWM is a powerful strategy for site suitability study. A case study in Coimbatore, India, is presented in this study using GIS-based multi-criteria analysis for locating C&D waste TS. The criteria for site suitability analysis are chosen based on literature review, regulations, and experts' opinions. Weights of the chosen criteria are estimated using analytic hierarchy process (AHP), and the final suitability map is created by weighted overlay analysis (WOA) in GIS environment. Results provide first-hand information for local decision makers to locate C&D waste transfer station in the chosen study region and report that 12% of the entire area is "highly suitable" for transfer station location.

Multi-Criteria Decision-Making Approach for Siting Sewer Treatment Plants in Muscat, Oman

Sewer Treatment Plants (STPs) are essential pieces of infrastructure given the growing scarcity of water sources due to the challenges of urbanization. The positioning of STPs is a complex multidimensional process that involves integrative decision-making approaches that consider multiple sustainability criteria to ensure their optimal placement. The Multi-Criteria Decision Method (MCDM) is a suite of approaches available to decision-makers when making systematic and scientifically informed decisions on siting wastewater treatment plants. Although MCDM methods have manifold applications in different geographic contexts, there is a paucity of studies employing MCDM models for the siting of STPs within the context of Oman. In this study, we assessed the locations of existing STPs and identified suitable locations for future STPs within the Muscat Governorate of Oman using a Multi-Criteria Decision-Making Analytic Hierarchy Process (MCDM-AHP) model in a Geographic Information System (GIS) environment. Eight factors were considered in the MCDM-AHP model: slope, elevation, proximity to built-up areas, airports, valleys, road networks, the sea, parks, and golf courses. Each factor was assigned priority weights based on its importance using the AHP method. Thematic maps were generated to categorize the potential sites into different suitability levels. The results showed that the coastal areas of A’Seeb and Bowsher were the most suitable locations for STPs, representing only 1.19% of the total study area. The novelty of this study stems from the perspective of an original application within the context of Oman, which has generated novel results and interpretations. This has significant implications for urban policy and planning with respect to better informing decision-makers with a systematic framework for efficient wastewater treatment.

Identifying flood vulnerable and risk areas using the integration of analytical hierarchy process (AHP), GIS, and remote sensing: A case study of southern Oromia region

Many people have died, and economies have been devastated by flooding, which is one of the world's most prevalent and catastrophic natural catastrophes. As a result of human activity on the land, the flooding patterns have changed. Inadequate drainage infrastructures, as well as various activities such as deforestation, are exacerbating the flood. In Ethiopia, southern Oromia is one of the regions affected by flooding, with the West Guji and Guji zones being the most frequently flooded places. People are exploiting natural vegetation for various purposes as a result of rapid population growth, resulting in deforestation and susceptibility to natural disasters such as floods. As a result, identifying flood-prone locations in great detail is essential for effective issue-solving. Thus, the study aims to locate and map flood-prone and flood-risk regions in southern Oromia utilizing Remote Sensing, analytical hierarchy Process (AHP), and Geographic Information system (GIS). Eight flood-causing factors, such as drainage density, rainfall, elevation, slope, Topographic wetness index (TWI), soil type, distance from the river, and LULC were developed in the GIS environment, and ERDAS Imagine software was employed to make land coverage and land usage data. The weighted overlay Multi-criteria evaluation (MCE) approach was then used to construct a flood-sensitive map based on the AHP pairwise comparison of eigenvector value. To determine flood risk, population density, a vulnerable flood map, and land usage/land coverage layer were overlaid, having equal weightage. The flooding vulnerability outcome in southern Oromia indicates that 0.3% (20.53km2), 31.4% (24,986.4km2), 62% (49,310.6km2), 6.39% (5081.98km2), and 0.19% (148.866) is, very high, high, moderate, low, and very low flooding vulnerable region correspondingly. From the flood risk map, it was estimated that 2% (1587.3km2), 19.3% (15,384.4km2), 75.5% (60,071.7km2), and 3.15% (2505.3km2) of the area were endangered to very high, high, moderate, and low, flood risk levels correspondingly. As a result, the study will assist the relevant authorities in formulating development policies based on the area's existing flood danger and risk.

Spatial analysis of deforestation factors in the Atlantic Forest Biome/Brazil

Deforestation is a phenomenon that generates social, environmental, and economic impacts and endangers the sustainability of natural resources. The Atlantic Forest is one of the most biodiverse and threatened biomes in the world. Despite its enormous importance, the Atlantic Forest is the most deforested region in Brazil since its European colonization. Therefore, given the relevance of identifying the drivers of deforestation in Brazilian biomes, the objective of this paper is to analyze the factors that conditioned deforestation in the Atlantic Forest Biome in Brazil between the years 2010 and 2020. Land use and land cover data and socioeconomic data processed in a Geographic Information System (GIS) environment were used, through correlation analysis and Geographic Weighted Regression (GWR). The covariates that presented the highest correlation values were the average number of cattle, area of temporary crops, and agricultural credit, with 0.708, 0.516, and 0.418 respectively. The GWR model presented an adjusted R² of 0.94, confirming the model's capability to explain 94% of the variations in deforestation. The variable ranching was the one that displayed the highest correlation with deforestation. In addition, the area under temporary crops and agricultural credit also had a significant influence on the occurrence of deforestation.

Geospatial modelling of mineral potential zones using data-driven based weighting factor and statistical index techniques

Mineral prospectivity models (MPMs) are significantly essential in delineating target zones with the optimum likelihood of containing a particular sought-after mineral deposit. This present study carried out mineral potential mapping over the Collette Prospecting Licence (PL) Area of north-western Ghana using bivariate data-driven spatial statistical models composed of statistical index (SI) and weighting factor (WF) approaches. In the first instance, the geographic coordinates of variously known locations of artisanal mining operations as well as high Au concentration locations were mapped during a field survey. As a result, 181 known locations of Au occurrences were identified, out of which 127 (70%) were selected randomly for training and creating the mineral prospectivity models, whereas the remaining 54 (30%) were used to assess and validate the accuracy of the predictive models produced. The efficacy of mineral prospectivity models generated enormously depends on the appropriate selection of mineral-related factors. In this study, the following mineral-related condition factors (evidential layers) comprising analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator, and geology were used. The aforementioned evidential layers were derived and sourced from geophysical and geological datasets, which were later prepared for the generation of the models in a geographic information systems (GIS) environment. Finally, the validation of the mineral prospectivity models generated was carried out by applying the receiver operating characteristics (ROC) curve. The estimated results based on the ROC plots obtained for the predictive models showed that the area under the ROC curve (AUC) scores obtained for the SI-based and WF-based mineral prospectivity models were respectively, 0.780 and 0.733. Hence, it can be concluded that both mineral predictive models created in this study produced reasonably good accuracy (AUC score greater than 0.7) in predicting the potential zones of gold mineralisation occurrences within the Collette PL Area of north-western Ghana. These MPMs can serve as essential models for mineral exploration programmes within the study area.

Reckoning flood frequency and susceptibility area in the lower Brahmaputra floodplain using geospatial and hydrological approach

AbstractClimate change has remarkably intensified the occurrence of floods around the globe. Flooding causes loss of life and property. Flood frequency analysis (FFA) is an important investigation and plays a key role in flood‐related studies. Geographically, the study area is confined in the lower Brahmaputra floodplains, flat slope, and rivers are braided in nature. Because of Heavy rainfall, major rivers in the area carry huge influxes of surged water during the summer period. Hence, disastrous flooding can be seen every year in the study region. The present study aims to model flood frequency using the hydrological data to understand the effects within the area. FFA approaches like Gumbel, Log Pearson type 3 (LP‐3), and Log‐Normal (LN) were used, and comparative analyses were done using water level data for the Manas, Aie, and Brahmaputra Rivers. Moreover, remote sensing and the geographic information system (GIS) environment were used to generate FFA‐based flood predictive inundation map at 5, 10, 50, 100, and 200 years of return periods. Here, Gumbel's distribution has found the best fit for all the rivers among the three. The distribution reveals that at a 200‐year return period, the highest water level would be increased by 1.45, 2.41, and 4 m for the Manas, Aie, and Brahmaputra Rivers, respectively. The study shows that almost 493.54 and 673.72 km2 of areas are expected to be submerged at 5 and 200‐year return periods according to Gumbel's distribution; LP‐3 distribution predicted 493.01 and 555.66 km2, and the log‐normal distribution method predicted 432.51 and 555.74 km2 of flood‐sensitive areas at 5 and 200‐year return periods, respectively. The FFA highlighted spatio‐temporal effects on the expansion of submerged areas. We hope that the findings of the present study will aid in the different flood hazard management strategies for future endeavors.

Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq

Iraq, including the investigated watershed, has endured destructive floods and drought due to precipitation variability in recent years. Protecting susceptible areas from flooding and ensuring water supply is essential for maintaining basic human needs, agricultural production, and industry development. Therefore, locating and constructing storage structures is a significant initiative to alleviate flooding and conserve excessive surface water for future growth. This study aims to identify suitable locations for Runoff Harvesting (RH) and dam construction in the Hami Qeshan Watershed (HQW), Slemani Governorate, Iraq. We integrated in situ data, remotely sensed images, and Multi-Criteria Decision Analysis (MCDA) approaches for site selection within the Geographical Information Systems (GIS) environment. A total of ten criteria were employed to generate the RH suitability maps, including topographic position index, lithology, slope, precipitation, soil group, stream width, land cover, elevation, distance to faults, and distance to town/city. The weights of the utilized factors were determined via Weighted Linear Combination (WLC) and Analytic Hierarchy Process (AHP). The resulting RH maps were validated through 16 dam sites preselected by the Ministry of Agriculture and Water Resources (MAWR). Findings showed that the WLC method slightly outperformed AHP regarding efficiency and exhibited a higher overall accuracy. WLC achieved a higher average overall accuracy of 69%; consequently, it was chosen to locate new multipurpose dams for runoff harvesting in the study area. The overall accuracy of the 10 suggested locations in HQW ranged between 66% and 87%. Two of these sites align with the 16 locations MAWR has recommended: sites 2 and 5 in the northwest of HQW. It is noteworthy that all MAWR dam sites were situated in medium to excellent RH zones; however, they mostly sat on ineffective geological localities. It is concluded that a careful selection of the predictive factors and their respective weights is far more critical than the applied methods. This research offers decision-makers a practical and cost-effective tool for screening site suitability in data-scarce rugged terrains.

Assessing groundwater potential zones and artificial recharge sites in the monsoon-fed Murredu river basin, India: An integrated approach using GIS, AHP, and Fuzzy-AHP

The sustainable management of water relies heavily on groundwater resources, and an important aspect of their effective utilization is artificial recharge. In the specific case of the Murredu watershed in India, a comprehensive approach is proposed in this research, integrating Geographic Information Systems (GIS), Remote Sensing (RS), and Multi-Criteria Decision Making (MCDM) techniques. The aim is to delineate groundwater potential zones (GWPZs) and identify suitable locations for artificial recharge. Several thematic layers, such as slope, drainage density, geomorphology, land use land cover (LULC), curvature, soil, topographic wetness index (TWI), rainfall, distance from the river, and elevation, were considered in this study. To ascertain the relative importance of these thematic maps in influencing groundwater availability and recharge, the Analytic Hierarchy Process (AHP) and Fuzzy-AHP techniques were employed to assign weights. Utilizing a weighted overlay technique within a GIS environment, the GWPZs map was generated. Within the study area, the GWPZs were categorised as poor, moderate, and high based on the AHP and Fuzzy-AHP models. According to the AHP model, 14.93% of the GWPZs in the region were classified as poor, 73.53% as moderate, and 11.54% as high. On the other hand, the Fuzzy-AHP model classified 14.02% as poor, 76.55% as moderate, and 9.44% as high. The accuracy of these classifications was verified using the receiver operating characteristic curve (ROC) and area under the curve (AUC), resulting in accuracy rates of 71.4% for AHP and 77.6% for Fuzzy-AHP. Additionally, the study employed RS and GIS techniques to develop a map by overlaying the drainage density and lineament density maps. This approach helped identify 126 potential sites for artificial recharge based on the best-performing Fuzzy-AHP model. The insights gained from this study facilitate the sustainable management and planning of groundwater resources, ensuring their availability and sustainability for future generations.

Urban flood risk assessment using AHP and geospatial techniques in swat Pakistan

The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment of flood risk is crucial for effective urban planning and disaster management. In current research study flood hazard index was developed using analytic hierarchy process (AHP) technique in combination with the geographical information system (GIS) environment in Swat, Pakistan. The study integrates various data sources, including topographic maps, land use/land cover information, rainfall data, and infrastructure data, to develop a comprehensive flood risk assessment model. The weights obtained from the AHP analysis are combined with geospatial data using a geographic information system (GIS) to generate flood risk maps. The flood hazard levels were categorized into five distinct classes: very low, low, moderate, high, and very high. Using the GIS-AHP approach, higher weights were assigned to rainfall, distance to river, elevation, and slope in comparison to NDVI, TWI, LULC, curvature, and soil type. The flood hazard map was then reclassified for each parameter. By overlaying these maps, it was determined that 5.6% of the total area is classified as very high flood risk, 52% as high risk, 39.3% as moderate risk, and 3.1% as low risk. The developed comprehensive flood risk assessment model in current study can identify high-risk areas, prioritize mitigation measures, and aid in effective urban planning and disaster management.

GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing

This study proposes a decision-making framework that integrates a routing model based on the geographic information system (GIS) and a genetic algorithm into a building-information modeling (BIM) environment to overcoming the limitations of the planning process of traditional street-based sporting events. There is a lack of research on improving the manually conducted decision-making processes for street-based sporting events. Moreover, previous routing studies were limited to GIS environments, and proposals for decision-making models integrated with BIM environments are lacking. In this study, the applicability of the framework was verified by presenting the variables of the existing GIS-based routing model as environmental variables to consider the impact of street-based sports events on a city. The evaluation model for the route selection was parameterized independently, such that its priority could be changed according to the user’s needs. Moreover, we integrated the data into BIM to create and analyze models that assess urban effects. This method is a decision-making system for policymakers and event planners to promptly conduct initial venue surveys through the technological integration of GIS–routing–BIM. Additionally, the GIS stipulated in this study can be applied to other cities. The Gwanghwamun area of Seoul, South Korea, was selected as the case study.

Evaluating the performance of machine learning methods and Geographic Information System (GIS) in identifying groundwater potential zones in Tehran-Karaj plain, Iran

Approaches based on Geographic Information Systems (GISs) are the most common and influential methods in identifying and mapping Groundwater Potential Zones (GWPZ). Moreover, employing Machine Learning (ML) algorithms in modeling the influencing parameters on GWPZ can improve the performance of these models. Therefore, the present study aims to assess the efficiency of ML methods, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGB) models, along with influencing factors on GWPZ in GIS environment. Hence, the Tehran-Karaj plain in Iran, with approximately 5156 km2 area and relatively diverse elevations (818 to 4374 m), including flat and mountainous areas, was selected as the study area. Information from 105 wells was collected and divided into 75% for training and 25% for validation of the models. The effects of twelve parameters were investigated in three categories: topography, hydrology, and vegetation/geology (four parameters for each item). Moreover, LR, RF, SVM, and XGB models were applied to calculate the scores of each influential factor and create maps of GWPZ for the study area. Results of assessing the accuracy of GWPZ methods using the Receiver Operating Characteristic curve (ROC) revealed that the highest Area Under the Curve (AUC) value (89%) was obtained for the XGB model compared to RF, LR, and SVM models (with values of 87.4, 76, and 75.3%, respectively). Moreover, XGB and RF show higher Kappa coefficient values, with the values of 80% and 65%, respectively. This indicated the tree-based models' high performance (i.e., XGB and RF) compared to other models. Model outputs also demonstrated that topographic parameters (i.e., altitude levels and slope) strongly influenced the GWPZ predictions of the used models in the study area. In general, results indicated the high performance of ML algorithms and GIS for identifying GWPZ.

Delineation of a Groundwater Potential Zone Map for the Kızılırmak Delta by Using Remote-Sensing-Based Geospatial and Analytical Hierarchy Processes

Locating prospective groundwater recharge zones is essential for managing and planning groundwater resources. Therefore, spatial modeling of groundwater distribution is a significant undertaking that would aid groundwater’s subsequent conservation and management. The current study employs geographic information systems (GIS) and analytical hierarchy process (AHP) based on multi-criteria analysis to identify groundwater potential zones (GPZ). The AHP technique was utilized to analyze and generate the geo-environmental factor parameters, which included drainage density, lineament density, precipitation, slope, soil texture, land use/land cover, groundwater level, and geology. Each factor is weighted according to its characteristics and effects on water potential in this process. Finally, the weighted overlay method was applied in a GIS environment to gather the weighted variables and locate the map of the GPZ. The proposed GPZ map is divided into four different groundwater potential zones: poor, moderate, high, and very high. Consequently, according to the results, 38% of the basin has very high groundwater potential, 19% has high potential, 39% has moderate potential, and 4% has poor potential from the spatial distribution of the potential regions. Therefore, the study’s conclusions can be used to sustain groundwater resources by identifying areas with high groundwater potential.