Search Results (201)

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the problem, adapting to changing conditions such as traffic or fluctuating demand. Methods: In this paper, we model and optimize a classic multi-link distribution network topology, including randomness in travel times, vehicle availability times, and product demands, using a hybrid approach of nested linear stochastic programming and Monte Carlo simulation under a time-window scheme. The proposed solution is compared with cutting-edge metaheuristics such as Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Results: The results suggest that the proposed method is computationally efficient and scalable to large models, although convergence and accuracy are strongly influenced by the probability distributions used. Conclusions: The developed proposal constitutes a viable alternative for solving real-world, large-scale modeling cases for transportation management in the supply chain. Full article

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. Full article

This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological data to generate timely, actionable recommendations for conservation interventions. These may include pre-emptive shutter closure during heatwaves, activation of ventilation under elevated humidity, or intensified monitoring of structurally sensitive zones during heavy precipitation. By coupling historical datasets with real-time telemetry and calibrated predictive models, GEPReS addresses the distinctive vulnerabilities of heritage structures, which arise from material sensitivity, conservation constraints, and operational limitations under contemporary climatic conditions. The architecture combines spatial analysis, typology-aware risk assessment, and reproducible modelling practices to ensure interpretability and compliance with conservation principles. Designed for scalability and online implementation, the system provides a modular framework capable of adapting to diverse building typologies and resource environments. The paper details the system architecture, data sources, modelling approach, and implementation challenges, supported by empirical evidence from multi-site pilot deployments. Full article

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R² (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis. Full article

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article

The Dhaka District is experiencing rapid industrial growth alongside uncontrolled urban expansion, leading to significant land-use conflicts and environmental pressures. This study investigates how to identify the optimal sites for industrial development that support sustainable urban growth by leveraging Geographic Information Systems (GISs), combined with a structured decision-making approach. The analysis incorporates key environmental and infrastructural factors to guide responsible planning aligned with global sustainability objectives. This study integrates spatial variables such as transport accessibility, land use, environmental sensitivity, and infrastructure presence. Up-to-date satellite imagery and land-use information from recent years ensure relevant and precise analysis. The findings indicate that roughly 10–15% of Dhaka District is suitable for industrial activities, predominantly the western and northwestern edges of the district. However, a considerable portion of existing industries are situated outside the officially designated zones, with nearly 9% infringing on protected environments, pointing to gaps in land management policies. Additionally, industrial expansion resulted in the conversion of over thousands of hectares of natural land, underscoring urgent ecological concerns. Scenario modeling further demonstrates how strategic land allocation can balance industrial growth with environmental conservation. This research highlights the value of integrating a GIS with multi-criteria evaluation using Analytical Hierarchy Process (AHP) to provide a flexible, data-driven framework for sustainable industrial land-use planning. Full article

High-resolution LiDAR-derived three-dimensional (3D) digital outcrop models are crucial for detailed geological analysis. However, their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing significant visualization challenges. Although Level of Detail (LOD) techniques are well-established in Geographic Information Systems (GISs) and computer graphics, they still require customized design to address the unique characteristics of geological outcrops. This paper presents an automated method for constructing and visualizing LOD models specifically tailored to high-resolution LiDAR outcrops. The workflow begins with segmenting the single-body model based on texture coverage, followed by building an adaptive LOD tile pyramid for each segment using a pseudo-quadtree approach. The proposed LOD construction method incorporates several innovative components: segmentation based on texture coverage, an adaptive LOD tile pyramid using a pseudo-quadtree, and a feature-preserving mesh simplification algorithm that includes vertex sharpness constraint and boundary freezing strategy to maintain critical geological features. For visualization, a dynamic multi-scale loading and rendering mechanism is implemented using an LOD index with the OpenSceneGraph (OSG) engine. The results demonstrate that the proposed method effectively addresses the bottleneck of rendering massive outcrop models. The models loading time and average memory usage were reduced by more than 90%, while the average display frame rate reached around 60 FPS. It enables smooth, interactive visualization and provides a robust foundation for multi-scale geological interpretation. Full article

In the São Brás de Alportel municipality, water scarcity poses a significant constraint on agricultural activities. This study utilises Remote Sensing (RS) and Geographical Information Systems (GISs) to identify existing irrigated areas, delineate catchment basins, and select the most suitable sites for the installation of new surface water reservoirs. First, the principal territorial components were characterised, including physical elements (climate, geology, soils, and hydrography) and anthropogenic infrastructure (road network and high-voltage power lines). Summer Sentinel-2 satellite imagery was then analysed to calculate the Normalised Difference Vegetation Index (NDVI), enabling the identification and classification of irrigated agricultural parcels. Flow directions and accumulations derived from Digital Elevation Models (DEMs) facilitated the characterisation of 38 micro-catchments and the extraction of 758 km of the drainage network. The siting criteria required a minimum setback of 100 m from roads and high-voltage lines, excluded farmland currently in use, and favoured mountainous areas with low permeability. Only 18.65% (2854 ha) of the municipality is agricultural land, of which just 4% (112 ha) currently benefits from irrigation. The NDVI-based classification achieved a Kappa coefficient of 0.88, indicating high reliability. Three sites demonstrated adequate storage capacity, with embankments measuring 8 m, 10 m, and 12 m in height. At one of these sites, two reservoirs arranged in a cascade were selected as an alternative to a single structure exceeding 12 m in height, thereby reducing environmental and landscape impact. The reservoirs fill between October and November in an average rainfall year and between October and January in a dry year, maintaining a positive annual water balance and allowing downstream plots to be irrigated by gravity. The methodology proved to be objective, replicable, and essential for the sustainable expansion of irrigation within the municipality. Full article

To mitigate the impacts of urbanisation and the attendant surface sealing, appropriate measures are required when adapting urban spaces and drainage infrastructure. In this context, the deployment of Sustainable Drainage Systems (SuDSs) has emerged as a viable alternative, delivering highly positive outcomes by enhancing hydrological, hydraulic and landscape performance while restoring ecosystem services to the community. This study evaluates the relative performance of five SuDS typologies, green roofs, bioretention cells, infiltration trenches, permeable pavements, and rain barrels, implemented in a 64 ha subbasin of the metropolitan area of Barcelona, Spain. Using Giswater integrated with the SWMM, the stormwater drainage network was modelled under multiple rainfall scenarios. Performance was assessed using two qualitative indicators, the junction index (I_j) and the conduit index (I_c), which measure surcharge levels in manholes and pipes, respectively. The results show that SuDS implementation affecting 42.8% of the drained area can enhance network performance by 35.6% and reduce flooded junctions by 67%. Among the typologies, rain barrels and bioretention cells were the most effective. The study concludes that SuDS construction, supported by open-source tools and performance-based indicators, constitutes a replicable and technically robust strategy for mitigating the effects of surface sealing and increasing urban resilience. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural language instructions provided by users and translating them into automated GIS workflows through dynamically generated Python scripts. An interactive graphical user interface (GUI), built using CustomTkinter, was developed to enable intuitive user interaction with GIS data and processes, reducing the need for advanced programming or technical expertise. We conducted an empirical evaluation of this approach through a comparative case study involving typical GIS tasks such as spatial data validation, data merging, buffer analysis, and thematic mapping using urban datasets from Pesaro, Italy. The performance of our automated system was directly compared against traditional manual workflows executed by 10 experienced GIS analysts. The results from this evaluation indicate a substantial reduction in task completion time, decreasing from approximately 1 h and 45 min in the manual approach to roughly 27 min using our LLM-driven automation, without compromising analytical quality or accuracy. Furthermore, we systematically evaluated the system’s factual reliability using a diverse set of geospatial queries, confirming robust performance for practical GIS tasks. Additionally, qualitative feedback emphasized improved usability and accessibility, particularly for users without specialized GIS training. These findings highlight the significant potential of integrating LLMs into GISs, demonstrating clear advantages in workflow automation, user-friendliness, and broader adoption of advanced spatial analysis methodologies. Full article

The growing demographic trend of an aging population, particularly in remote rural areas, exacerbates social isolation and limits access to essential goods and services. This vulnerability highlights a pressing need to develop sustainable solutions for their mobility and support. Using Geographic Information Systems (GISs) and network analysis, a workflow was developed to optimize road-based transport for the elderly. The analysis utilized an electric vehicle, with its range limitations, influenced by road slopes, being a critical variable for assessing route efficiency. Two potential solutions were investigated: (1) the delivery of goods and medicines and (2) the transport of passengers and medicines. The methodology was tested using the Municipality of Seia, Portugal, as a case study, with a defined weekly visit frequency. The results demonstrate that both proposed solutions are technically viable for implementation, with the transport of passengers and medicines being the most effective option. This study provides a foundational framework for developing practical, demand-oriented, sustainable transport and logistics services to support isolated elderly populations. Full article