Geographic Information System Research Articles

Site selection for solar power plants using GIS and Fuzzy Analytic Hierarchy Process: Case study of the Western Mediterranean Region of Turkiye

As global warming becomes increasingly evident, the need to use renewable energy sources cannot be overstated. The consumption of fossil fuels in energy production not only exacerbates the effects of global warming but also negatively affects air quality and puts human health at serious risk. The objective of this research is to determine the most suitable locations for solar power plants (SPPs) in the Turkish provinces of Antalya, Burdur, and Isparta, which are situated in the Western Mediterranean Region (WMR). The study employs the Fuzzy Analytic Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) method, in conjunction with Geographic Information Systems (GIS) for the extraction of spatial information. In evaluating SPP site selection, 11 criteria were considered, including climate, economy, topography, and environmental factors. To produce more objective results during the decision-making phase, a thorough analysis of the relationship between solar irradiance and climatic factors such as air temperature, cloud frequency, and water vapor density which are crucial for the power plant's efficiency in SPP projects was conducted using machine learning techniques. The criteria weights were calculated by the FAHP method, considering expert opinions, literature observations, and machine learning results. The results show that approximately 20% of the region is suitable for SPP.

Predicting wind turbine energy production with deep learning methods in GIS: A study on HAWTs and VAWTs

The increasing global demand for renewable energy necessitates accurate forecasting methods to optimize wind energy production, particularly in regions with varying climatic conditions. This study addresses this need by utilizing advanced deep learning techniques and Geographical Information Systems (GIS) to estimate the energy output of wind turbines. Specifically, it focuses on predicting the energy production of both horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) using a combination of Markov and Cellular Automata-Markov (CA-Markov) models, alongside deep learning methods such as long short-term memory (LSTM), LSTM-Wavelet, and Support Vector Regression (SVR). Additionally, the study evaluates the energy output of each turbine type, factoring in their construction costs within the study area. The analysis reveals significant variations in energy output over time, with maximum values increasing from 85,017 Wh in 2000 to 166,050 Wh in 2020 in the northern region, while minimum outputs also rose significantly. Projections for 2030 suggest that approximately 17% of the northern region experience a substantial increase in wind power potential. Among the forecasting methods, the LSTM-Wavelet hybrid model demonstrated superior accuracy, surpassing the 90% threshold, primarily due to its effective handling of data instability and noise reduction. This study underscores the potential of using sophisticated modeling techniques to enhance wind energy forecasting, contributing to more efficient energy management in regions with high energy demand and limited resources.

Percursos de pedestres: um índice a partir das inter-relações entre caminhabilidade e conforto ambiental

Abstract The study of environmental comfort in open urban spaces has undergone constant updates in the last decades. In the meantime, the specific analysis of these aspects within the context of walkability stands out as an area with considerable potential to be investigated. In this context, this article aims to explore walkability in pedestrian routes and its relationship with environmental comfort, proposing a more systematized approach for its application in the strategic planning of cities. Therefore, we proposed the construction of a Pedestrian Walkability and Comfort Index (ICCP), composed of three partial indices that consider thermal, acoustic, and ergonomic aspects of pedestrian paths. The methodology is based on street visualization tools and Geographic Information System (GIS) tools to spatialize and systematize the interactions between walkability and environmental comfort. The research concludes that ICCP is a valid tool for measuring walkability at meso and micro scales, and it can be replicated in other urban contexts. Furthermore, the development and application of ICCP also highlight that rethinking walkability with environmental comfort in urban areas requires an integrated approach to the entire mobility system and urban infrastructure of cities.

Spatial analysis of environmental sanitation and stunting incidents

Environmental factors such as sanitation and hygiene, drinking water sources, quality of drinking water, and ownership of latrines are indirect factors causing stunting. This study aims to analyze the distribution of stunting and environmental sanitation factors that cause stunting in Dairi District, North Sumatra, Indonesia. The case-control design was carried out with 172 toddlers. The distribution pattern of stunting and the association of patient characteristics and environmental risk factors was carried out using the Geographic Information System. Data on healthy clean-living behavior was collected using a questionnaire, and microbiological laboratory tests measured water quality. The spatial analysis uses an average nearest-neighbor overlay. The association of characteristics and risk factors with the incidence of stunting was analyzed using chi-square and logistic regression. The average nearest neighbor analysis shows that the nearest neighbor index is 0.19 ( 0.05) are unrelated to stunting. The pattern of distribution of cases tended to be in clusters, and no relationship was found between population density and the incidence of stunting. The findings of this study provide a new understanding that health promotion to prevent stunting does not only focus on fulfilling nutrition but also on healthy clean-living behavior and water quality.

Application of Z-number based fuzzy MCDM in solar power plant location selection problem in Spatial planning

In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.

Energy, environmental and economic performance of bi-facial photovoltaic noise barrier applied in city scale

Solar energy in transportation reduces fossil fuel consumption and supports carbon neutrality. Photovoltaic noise barriers (PVNB) are used globally to harness renewable energy along roads. This study's novelty lies in the detailed consideration of road orientation and the application of bifacial photovoltaic technology when assessing the potential for large-scale deployment of PVNB. Bifacial PV technology captures solar radiation from both sides, enhancing energy efficiency. A mathematical model was established and validated to predict PVNB power output at 5° azimuth intervals. Geographic information system (GIS) technology was used to quantify trunk roads, motorways, and RNB in Shenzhen, China. Accordingly, the electricity generation potentials of PVNB, as well as the economic and environmental benefits were calculated. The installed capacity and power generation of PVNB in Shenzhen are 432.29 MW and 313.37 GWh, respectively. The levelized cost of electricity is 0.4781 CNY/kWh. Compared to a 600 MW coal-fired unit, 9119.09 tce of fossil fuel can be saved, with lifecycle carbon reduction of 3.80 Tg and net carbon reduction of 3.05 Tg.

Hydrological Analysis of Wadi Arab Valley Dam by Integrate Soil Conservation Service and Geographic Information Systems

Hydrological Analysis of Wadi Arab Valley Dam by Integrate Soil Conservation Service and Geographic Information Systems

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Quantitative risk assessment of road exposed to landslide: A novel framework combining numerical modeling and complex network theory

The quantitative analysis of the landslide risk posed to road networks is a challenging task owing to the uncertainty involved both in the potential landslide hazard and the road value. To address this challenge, this paper proposes a novel framework to assess the road risk in quantitative terms. The landslide hazard is assessed using the depth-integrated method with consideration of the landslide size probability and initial conditions. The potential direct losses associated with road disruption are determined by exposure analysis in a geographical information system. The indirect losses of the road caused by the landslide were analyzed through complex network theory with consideration to regional socioeconomic development and the time required for road restoration. The proposed framework was used to assess the road risk posed by the Chunchangba landslide, Xiaojin County, China. The results show that the volume size probability of landslides in the Xiaojin area could be assessed using the function PVL=1/1+3.583×10−5VL2×0.391. The depth-integrated method based hazards assessment results show the maximum impact area of the Chunchangba landslide reached 0.383 km2, and the landslide has a high probability of damaging the lower road and forming a barrier lake. The losses associated with road disruption caused by the landslide were estimated. The maximum direct losses reached 293,100 USD, while indirect losses reached 423,800 USD, which has the same importance as direct losses. The risk curve reveals that the maximum probability of the road risk associated with the Chunchangba landslide is 0.0175 %, 0.0189 %, 0.0190 %, and 0.0191 % for the time interval of 5, 10, 20, and 50 years, respectively, with losses of 0.177 million USD/year. The regional disaster mitigation strategy is analyzed based on quantitative risk analysis. The results show that a new 2.7 km road on the mountain opposite the Chunchangba landslide can reduce indirect losses by approximately 300 times. The findings of this study contribute to sustainable development and landslide risk management in mountainous areas.

Multi-Criteria Decision Making (MCDM) Approach for Identifying Optimal Solar Farm Locations: A Multi-Technique Comparative Analysis

This study explores the spatial suitability for solar farm placement using Geographic Information System (GIS) techniques (ArcGIS Pro 3.0.2) coupled with Muti-Criteria Decision-Making (MCDM) methods. Nine evaluation criteria significantly impacting solar farm placement in Erbil, Iraq were selected, and the Analytic Hierarchy Process (AHP) was utilized to assign proper weights to these criteria. Suitability maps were derived and classified using three MCDM methods: Weighted Overlay (WO), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting) to establish comparisons. Discrepancies were noticeable between the outcomes of these methods, with distinctions observable across most categories. The validation process illustrated superior performance by SAW model, closely followed by TOPSIS model, while the weighted overlay model showed significant differences. The southeastern region of Erbil governorate was identified as the most suitable area for solar farm construction due to favorable criteria values and potential for future expansion. This approach massively facilitates locating and evaluating ideal areas for solar farm construction, thereby minimizing the time, financial resources, and efforts dedicated towards this endeavor.

The first step towards FAIR-ness in Bulgarian archaeology: The Archaeological Map of Bulgaria in ARIADNE and ARIADNEplus

Summary This article provides an overview of the participation of the National Archaeological Institute with Museum at the Bulgarian Academy of Sciences (NAIM-BAS) as a partner in both the ARIADNE and ARIADNEplus projects and the SEADDA COST Action. The article examines both the workflow and the results obtained when modifying and mapping part of the national site and monuments information system, also known as the 'Archaeological Map of Bulgaria' (AIS AKB). The article's focus is mainly on the technical preparation of data shared through the ARIADNE portal. The main workflow followed throughout the projects included mapping terms to the Getty AAT thesaurus, adding Bulgarian archaeological chronologies to PeriodO, and mapping metadata to the ARIADNE catalogue data model (ACDM) in ARIADNE and to the AO-cat ontology in ARIADNEplus. In 2016, a mapping of AIS AKB to the CIDOC CRM was also undertaken, and the first steps towards creating the geographic information system, 'Archaeological Map of Bulgaria' (GIS AKB), were taken. The experience gathered by the NAIM-BAS team members throughout the whole project was important for moving towards FAIR-ness in Bulgarian archaeology.

Commuter Cyclist Crash Incidence Rate on Cycling Infrastructure in Lyon and Marseille (France)

The aim of this study is to document the crash incidence rate for cyclists when cycling on five different types of cycling infrastructure in two cities in France. A questionnaire was carried out in 2021 with commuter cyclists in Lyon-Villeurbanne (n=780) and Marseille-Aix-en-Provence (n=404). Respondents provided data on their regular cycling trip and on crashes they have experienced. Using a Geographical Information System, the exposure of cyclists on each type of infrastructure was determined. By relating the number of crashes to the number of kilometres travelled, crash rates (per kilometre travelled) and the 95 percent confidence interval were estimated. This study shows that in the two cities studied, the crash incidence rate is significantly higher on contraflow cycle lanes than on the road. On the contrary, shared-use paths (greenways) are associated with a lower crash incidence rate compared to the road. Results for the other types of cycling infrastructure are unclear. Recommendations are discussed. Further studies are required to improve the design of cycling infrastructure and the broader transport policy to support safe cycling.

Designing a Multitemporal Analysis of Land Use Changes and Vegetation Indices to Assess the Impacts of Severe Forest Fires Before Applying Control Measures

Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies.

India's flood risk assessment and mapping with multi-criteria decision analysis and GIS integration

ABSTRACT India's diverse geography poses significant flood risks, addressed in this study through the geographic information system and multi-criteria decision analysis. This comprehensive flood risk assessment considers seven parameters: mean annual precipitation, elevation, slope, drainage density (DD), land use and land cover, proximity to roads, and distance to rivers. The findings indicate that flood vulnerability is primarily influenced by rainfall, elevation, and slope, with DD, land use, and proximity to roads and rivers also playing crucial roles. Experts weighed these factors to create a thorough flood risk map using the normalized rank index and normalized weight index, categorizing areas into five risk levels: very high, high, moderate, low, and very low. The study reveals that 3.40% of the area is at very high risk, 32.65% at high risk, 39.72% at moderate risk, 20.97% at low risk, and 3.25% at very low risk. These results highlight how human and natural factors interact to influence flood risk, with vulnerable areas characterized by low elevations, steep slopes, high drainage densities, and proximity to rivers or roads. The findings provide valuable insights for policymakers, scientists, and local authorities to develop strategies to mitigate flood losses across India's varied landscapes.

Unveiling Groundwater Potential in Hangu District, Pakistan: A GIS-Driven Bivariate Modeling and Remote Sensing Approach for Achieving SDGs

Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and Frequency Ratio (FR) models to delineate Groundwater Potential Zones (GWPZs) in the Hangu District, a hydrologically stressed riverine region in northern Pakistan, to support the Sustainable Development Goals (SDGs). Ten key variables, including elevation, slope, aspect, distance to drainage (DD), rainfall, land use/land cover, Normalized Difference Vegetation Index, lithology, and road proximity, were incorporated into the Geographic information system (GIS) environment. The FR model outperformed the WoE model, achieving success and prediction rates of 89% and 93%, compared to 82% and 86%. The GWPZs-FR model identified 23% (317 km2) as high potential, located in highly fractured pediment fans below 550 m, with gentle slopes (<5 degrees), DD (within 200 m), and high rainfall in areas of natural trees and vegetation on valley terrace deposits. The research findings significantly support multiple SDGs, with estimated achievement potentials of 37.5% for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions of 10% for SDG 2 and 5% for SDG 3. This approach provides valuable insights for policymakers, offering a framework for managing groundwater resources and advancing sustainable practices in similar hydrologically stressed regions.

A Novel Rainfall Classification for Mapping Rainwater Harvesting: A Case Study in Kalar, Iraq

Increasing water demand driven by population growth and climate change strains water resources, especially in arid regions. The effectiveness of rainwater harvesting (RWH) as a viable solution is contingent upon the meticulous selection of appropriate sites. Contemporary efforts have increasingly utilized Geographic Information Systems (GIS) and remote sensing technologies to optimize the identification of ideal locations for implementing RWH infrastructure. However, inconsistencies in rainfall classification methodologies can compromise the accuracy of the resulted suitability maps. Consequently, a standardized approach to grading rainfall depth for mapping RWH sites becomes imperative. This study presents an innovative rainfall classification method tailored for both micro and macro catchment areas, offering a reliable and adaptable approach to rainfall analysis. By refining classification criteria, this method aims to improve the consistency and precision of RWH mapping, addressing a gap in existing methodologies and providing a more standardized approach. Through the application of FAHP and Fuzzy overlay techniques in ArcGIS 10.4, the study compares traditional rainfall classification with the proposed new classification method to assess RWH suitability in Kalar. The comparison highlights that the new rainfall classification-based map yielded higher accuracy and realism compared to traditional methods.

Refining Dijkstra's Algorithm: Employing Smart Solutions for Efficient Waste Management and Pollution Mitigation

This study explores the application of Dijkstra's algorithm to enhance waste disposal management systems, focusing on optimizing routing for efficient waste disposal. By modeling waste disposal pathways, the research aims to identify the shortest and most cost-effective routes, thereby reducing both pollution and operational costs. The analysis demonstrates that Dijkstra's algorithm can effectively streamline waste collection processes in Abakaliki, Ebonyi State, Nigeria, enabling the waste management board to minimize distance, time, and expenses associated with waste transportation. The developed model is both scalable and adaptable, promoting the integration of Geographic Information Systems (GIS) and real-time data for dynamic route optimization. The findings suggest significant potential for this approach not only in waste management but also in other domains such as traffic management and urban planning. This paper advocates for further exploration of user-friendly interfaces and alert systems to enhance operational efficiency. This research highlights the overall transformative potential of Dijkstra's algorithm in improving the sustainability and effectiveness of municipal services.

Leveraging GIS for sustainable tourism development: A comprehensive spatial approach

Abstract. This paper explores the application of Geographic Information Systems (GIS) as a pivotal tool in the spatial analysis and sustainable development of tourism. By mapping tourism resources, analyzing visitor flows, and planning for accessibility and infrastructure, GIS enables a nuanced understanding and management of tourism's spatial dynamics. We delve into the innovative use of GIS for personalized itinerary planning, augmented reality experiences, safety, risk management, environmental impact assessments, resource management, conservation, stakeholder engagement, and community planning. Through a series of methodical processes, GIS facilitates the visualization of natural and cultural attractions, optimizes visitor experiences, and ensures the sustainability of tourism development. The integration of spatial data with advanced analytical tools supports the creation of dynamic, inclusive, and environmentally responsible tourism strategies. This paper highlights the critical role of GIS in shaping future tourism practices, underlining the importance of spatial analysis in fostering a balanced approach to tourism that benefits local communities, preserves cultural heritage, and minimizes ecological footprints.

Determining the Optimal Fire Station Locations in Baghdad Al-Rusafa Using GIS Models and Data

The study of sitology has a long historical background and has recently attracted the attention of many researchers. One of its most important branches is the location of fire stations, which are of great importance in major cities. One of the most important features of an appropriate location is increasing coverage and reducing access time. In this research, geographic information systems were used to choose the most appropriate sites on the Al-Rasafa side in the city of Baghdad/Iraq to facilitate access to the scene of the accident as soon as possible from the fire stations and through the use of the geographical database of the city of Baghdad. GIS analysis was used to build the mathematical model that was adopted based on a number of criteria, including land use, distance to commercial malls, government and educational institutions, hospitals, and distance from existing fire stations. A weight was given to each of these criteria using the Overlay Weighted tool from spatial analysis in geographic information systems to determine the most appropriate areas. Then the road network was added to the criteria used, and then the selection was made on the areas close to the main roads, so the search results were to determine the best sites in Al-Karrada, Al-Sadr City, and Al-Adhamiya.