Search Results (301)

Coastal erosion represents a pervasive issue affecting numerous coastal regions, stemming from both natural phenomena and anthropogenic activities. Notably, a substantial proportion, approximately 70%, of sandy beaches globally exhibit a retreating trend. This study aims to clarify the coastal erosion dynamics that have undergone significant transformation in recent decades, exerting a profound impact on the coastal systems along the Italian peninsula. Specifically, this study investigates a segment of the Lazio coastline corresponding to the Foce Verde—Rio Martino beach area in the Latina municipality. Geographic Information System (GIS) software, such as ArcGIS Pro 3.5.0, was employed for geospatial data acquisition, enabling the precise delineation and documentation of shoreline fluctuations within this coastal expanse spanning from 2003 to 2019 (the inclusion criteria for the core research period of the Bachelor’ s thesis, along with the graduation year). The principal objective of this investigation is to furnish a comprehensive overview of the metamorphosis observed in the Latina coastline during the specified temporal interval. This analysis will encompass an evaluation of the coastal defense mechanisms employed, encompassing both “hard” (engineered structures) and “soft” (natural or nature-based) interventions, within this temporal context. Full article

Objective: Pediatric cancer, though less prevalent than adult malignancies, constitutes a significant public health concern due to its long-term effects on survival, development, and quality of life. This study aimed to investigate spatial patterns and temporal trends of pediatric cancer in Romania over a ten-year period (2008–2017), identifying persistent and emerging geographic hotspots using Geographic Information Systems (GIS)–based modelling and spatial statistics. Methods: A national pediatric cancer registry provided by the Ministry of Health was analyzed for cases among individuals aged 0–18 years, categorized by administrative-territorial units (ATUs), ICD-10 codes, sex, and year. Spatial indicators of persistence (recurrent prevalence across multiple years) and continuity (uninterrupted recurrence) were computed. Hotspot analysis was conducted using Local Moran’s I, and trend patterns were assessed through temporal modeling. Additionally, fractal and complexity metrics were applied to characterize the spatial structure and heterogeneity of cancer persistence and continuity across regions. Results: Although national pediatric cancer prevalence exhibited a modest decline from 3.57‰ in 2008 to 3.44‰ in 2017, GIS-based spatial modeling revealed stable high-risk clusters in Central and South-Eastern Romania, particularly in historically industrialized counties such as Hunedoara, Prahova, and Galați. These correspond to regions with past heavy industry and chemical pollution. Male children presented a higher frequency of malignant tumors (48,502 cases in males vs. 36,034 in females), while benign and uncertain-behavior neoplasms increased more prominently among females (from 3847 to 4116 cases, compared with 3141 to 3199 in males). Several rural localities showed unexpected prevalence spikes, potentially associated with socioeconomic deprivation, limited health literacy, and reduced access to pediatric oncology services. Regional disparities in diagnostic and reporting capacities were also evident. Conclusion: GIS-based spatial epidemiology proved effective in revealing localized, sex-specific, and persistent disparities in pediatric cancer across Romania. The integration of spatial indicators and complexity metrics into national cancer control programs could strengthen early detection, optimize resource allocation, and reduce health inequities. These findings highlight the value of combining geospatial analysis and fractal modeling to guide evidence-based public health strategies for pediatric oncology. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. 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

Transition to electric vehicles has accelerated in diverse consumer sectors all over the world. Electric School Buses (ESBs) are a particular area of interest due to their environmental and financial potential benefits, including Vehicle-to-Grid (V2G) synergies. Storing electricity in times of lower demand to supply the grid at optimal times can provide significant sustainability benefits, among them a reduction in new generation capacity and financial revenue for battery owners. ESBs, with their high-capacity batteries, have significant potential to supply the grid in V2G systems. There are more than half a million school buses in the US, with a wide geographical distribution, which have significant idle times during school days and holidays. This presents very attractive investment possibilities, providing school districts with additional revenue and supplying local communities with sustainable electricity at high-demand times. This study develops a framework to financially evaluate sustainability of ESB V2G schemes in the US. It applies data analytics, GIS, and Business Intelligence to integrate and assess publicly available data to provide stakeholders with decision-making tools in selecting optimal locations and operation times for these projects. Results indicate that revenue for these projects is significant in most schools, with some locations generating very high revenue potential. Geospatial analysis for most locations and time frames indicates very promising results, with schools potentially receiving significant income from these systems. The framework provides, therefore, relevant information for stakeholders to make sustainable decisions on the development of these projects. Full article

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. This paper presents the Study Area Map Generator, a web-based application developed using Shiny for Python, designed to automate the creation of country- and city-level study area maps. The tool integrates geospatial data processing, cartographic rendering, and user-friendly customization features within a browser-based interface. It enables users—regardless of GIS proficiency—to generate publication-ready maps with customizable titles, basemaps, and inset views. A usability survey involving 92 participants from diverse professional and geographic-based backgrounds revealed high levels of satisfaction, ease of use, and perceived usefulness, with no significant differences across GIS experience levels. The application has already been adopted in academic and policy contexts, particularly in low-resource settings, demonstrating its potential to democratize access to cartographic tools. By aligning with open science principles and supporting reproducible workflows, the Study Area Map Generator contributes to more equitable and efficient scientific communication. The application is freely available online. Future developments include support for subnational units, thematic overlays, multilingual interfaces, and enhanced export options. Full article

Heavy metal contamination of aquatic systems represents a critical environmental and public health concern due to the persistence, toxicity, and bioaccumulative potential of these elements. Geographic information systems (GISs) have emerged as indispensable tools for the spatial assessment and management of heavy metals (HMs) in water resources. This review systematically synthesizes current research on GIS applications in detecting, monitoring, and modeling heavy metal pollution in surface and groundwater. A bibliometric analysis highlights five principal research directions: (i) global research trends on GISs and heavy metals in water, (ii) occurrence of HMs in relation to World Health Organization (WHO) permissible limits, (iii) GIS-based modeling frameworks for contamination assessment, (iv) identification of pollution sources, and (v) health risk evaluations through geospatial analyses. Case studies demonstrate the adaptability of GISs across multiple spatial scales, ranging from localized aquifers and river basins to regional hydrological systems, with frequent integration of advanced statistical techniques, remote sensing data, and machine learning approaches. Evidence indicates that concentrations of some HMs often surpass WHO thresholds, posing substantial risks to human health and aquatic ecosystems. Furthermore, GIS-supported analyses increasingly function as decision support systems, providing actionable insights for policymakers, environmental managers, and public health authorities. The synthesis presented herein confirms that the GIS is evolving beyond a descriptive mapping tool into a predictive, integrative framework for environmental governance. Future research directions should focus on coupling GISs with real-time monitoring networks, artificial intelligence, and transdisciplinary collaborations to enhance the precision, accessibility, and policy relevance of heavy metal risk assessments in water resources. Full article

The interaction between humans and water has historically shaped landscapes, in which rivers played a central role in the development of territories. Among the infrastructures developed to manage water resources, watermills had always represented a key element of minor cultural heritage, reflecting centuries of adaptation to environmental, economic, and technological conditions. Although once central to river landscapes, these structures are now largely forgotten and at risk of being lost, particularly in Italy’s inner areas affected by rural depopulation, climate change, and natural hazards. The case analyzed in this paper, part of a larger research project, focuses on the analysis of watermills in the Aterno River valley in the Abruzzo region of central Italy. This fragile mountainous area is currently threatened by natural hazards and depopulation. The aim is to fill the gap in documentation on this “minor heritage,” which has been identified and cataloged, along a timeline of its vulnerabilities, starting from historical cartography, integrated with a localized field survey within a geographic information system. The GIS facilitates the cross-referencing of historical, geospatial, and environmental data, including hydrogeological and flood risk information. The results demonstrate how water, once a resource, has become a vulnerability factor and highlights the fragility of these historic artifacts, contextualized within the surrounding landscape. Full article

This research presents a comprehensive spatiotemporal assessment of the effects of climate change and anthropogenic pressures on the water surface area and quality of the Sidi Salem Dam, the largest reservoir in Northern Tunisia. Located within a sub-humid to Mediterranean humid bioclimatic zone, the dam plays a vital role in regional water supply, irrigation, and flood control. Utilizing a 40-year dataset (1985–2025), this study integrates multi-temporal satellite imagery and geospatial analysis using Geographic Information System (GIS) and remote sensing (RS) techniques. The temporal variability of the dam’s surface water extent was monitored through indices such as the Normalized Difference Water Index (NDWI). The analysis was further supported by climate data, including records of precipitation, temperature, and evapotranspiration, to assess correlations with observed hydrological changes. The findings revealed a significant reduction in the dam’s surface area, from approximately 37.8 km² in 1985 to 19.8 km² in 2025, indicating a net loss of 18 km² (47.6%). The Mann–Kendall trend test confirmed a significant long-term increase in annual precipitation, while annual temperature showed no significant trend. Nevertheless, recent observations indicate a decline in precipitation during the most recent period. Furthermore, Pearson correlation analysis revealed a significant negative relationship between precipitation and temperature, suggesting that wet years are generally associated with cooler conditions, whereas dry years coincide with warmer conditions. This hydroclimatic interplay underscores the complex dynamics driving reservoir fluctuations. Simultaneously, land use changes in the catchment area, particularly the expansion of agriculture, urban development, and deforestation have led to increased surface runoff and soil erosion, intensifying sediment deposition in the reservoir. This has progressively reduced the dam’s storage capacity, further diminishing its water storage efficiency. This study also investigates the degradation of water quality associated with declining water levels and climatic stress. Indicators such as turbidity and salinity were evaluated, showing clear signs of deterioration resulting from both natural and human-induced processes. Increased salinity and pollutant concentrations are primarily linked to reduced dilution capacity, intensified evaporation, and agrochemical runoff containing fertilizers and other contaminants. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article