Search Results (5,861)

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO${}_2$ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented in the Enipeas River basin, located within the Thessalia River Basin District, Greece. Hydrological analysis was performed using the HEC-HMS software (version 4.12), while hydraulic simulations were conducted with HEC-RAS 2D. The hydraulic modeling produced synthetic flood scenarios for a 1000-year return period, generating spatially distributed outputs of flood extents. The deep learning algorithm was based on a U-Net (CNN) architecture. The model was trained using multi-channel raster tiles, including open access geospatial data such as Digital Elevation Model, slope, flow direction, stream centerline, land use, and simulated flood extents. Model validation was carried out in two independent domains (TS1 and TS2) located within the same river basin. Model outputs are adequately compared with both 2D hydraulic simulations and official Flood Risk Management Plan maps, and the comparison indicates close spatial and quantitative agreement, with flood extent area differences below 8%. Based on the results, the proposed methodology presents a potential and efficient tool for rapid flood risk mapping. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

Land-use and land-cover change (LULCC) is a crucial indicator of environmental transformation and has significant implications for biodiversity, ecosystem services, and climate change. This study investigates land-cover changes between 2017 and 2023 in three distinct mountain regions: the Bucegi Mountains, the Allgäu High Alps, and Mount Olympus. Using remote-sensing data from Sentinel 2 and Geographic Information System (GIS) tools, we analyzed temporal shifts in land-cover types across these regions. The analysis highlights the varying rates and patterns of land-cover transformation in response to environmental and anthropogenic factors. Additionally, the MOLUSCE model was employed to predict future land-cover changes for the year 2029. The findings emphasize the dynamic nature of land-cover in these mountainous areas and offer insights into the potential environmental implications of predicted changes. The Bucegi and the Olympus regions experienced minor land-use changes, while the Allgäu High Alps have the most dynamic changes. The study contributes to a deeper understanding of land-cover dynamics and the applicability of remote sensing and GIS-based predictive models in ecological monitoring. Full article

The unique geo-ecological conditions of karst river basins (KRBs) heighten rural vulnerability to compound disturbances; therefore, enhanced rural resilience (RR) is critical for regional ecological security and sustainable development. In this study, the Wujiang River Basin was chosen as the study area. A comprehensive evaluation index system was first established to assess RR. Key driving factors were identified using the Optimal Parameters-based Geographical Detector (OPGD) mode. The Geographically and Temporally Weighted Regression (GTWR) model was then applied to analyze the spatiotemporal heterogeneity in the driving mechanisms of RR. Our results show that from 2010 to 2022: (1) RR in the study area increased significantly, and disparities among counties decreased notably, indicating a trend toward more balanced regional development. (2) RR displayed strong positive spatial autocorrelation, with spatial clusters evolving dynamically under the influence of policy interventions and environmental constraints. (3) The main drivers of spatial heterogeneity in RR included urban–rural income disparity, road network density, agricultural machinery power, etc. Their driving mechanisms exhibited significant spatiotemporal non-stationarity. The findings inform the development of targeted strategies to enhance regional resilience. Additionally, the methodology and empirical insights can serve as valuable references for RR research and practice in other similar KRBs worldwide. 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

Cross-ecosystem transmission of plant pathogens from crops to natural forests is increasingly recognized as a key factor in disease emergence and biodiversity loss. Agricultural systems serve as major sources of inoculum, with landscape interfaces—such as crop–forest edges, riparian zones, abandoned orchards, and nursery–wildland transitions—acting as active epidemiological gateways. Biological vectors, abiotic dispersal, and human activities collectively enable pathogen movement across these boundaries. Host-range expansion, recombination, and hybridization allow pathogens to infect both cultivated and wild hosts, leading to generalist and recombinant lineages that survive across diverse habitats. In natural ecosystems, such introductions can alter community composition, decrease resilience, and intensify the impacts of climate-driven stress. Advances in molecular diagnostics, genomic surveillance, environmental DNA, and remote sensing–GIS (Geographic Information System) approaches now enable high-resolution detection of pathogen flow across landscapes. Incorporating these tools into interface-focused monitoring frameworks offers a pathway to earlier detection, better risk assessment, and more effective mitigation. A One Health, landscape-based approach that treats agro–wild interfaces as key control points is essential for reducing spillover risk and safeguarding both agricultural productivity and the health of natural forest ecosystems. Full article

The decision-making process for the adaptive reuse of cultural heritage requires the evaluation of multiple criteria because of its multifaceted structure. The criteria determined through a literature review were weighted by experts and ranked according to their degree of importance via the DEMATEL method, which is a multicriteria decision-making technique. This study, conducted by integrating the importance levels of the criteria determined by the DEMATEL method with Geographic Information Systems (GIS) techniques, was applied to Yediocak Primary School, one of the significant buildings in Osmaniye, affected by the 2023 Kahramanmaraş Pazarcık Earthquake and heavily damaged during the event. The DEMATEL analysis demonstrated that economic value, regional potential, and compatibility with the new function are the primary cause-group criteria, whereas architectural, cultural, and social values are predominantly situated within the effect group. The spatial assessment yielded a low suitability score for the current primary school function (0.3954). The hybrid DEMATEL + GIS index (0.2598) confirmed that a building’s reuse as a high-occupancy school is constrained by seismic risk, its position on a heavily trafficked corridor, and relatively limited access to healthcare and emergency assembly areas. This study aimed to establish a new framework for the adaptive reuse of historic buildings. Full article

Out-of-hospital cardiac arrest (OHCA) is a major cause of death during mountain activities in the Alpine regions. Due to the time-critical nature of these emergencies and the logistical challenges of remote terrain, emergency medical services (EMS) are investigating the use of unmanned aerial vehicles (UAVs) to deliver automated external defibrillators (AEDs). This study presents a geospatial strategy for optimising AED delivery by UAVs in mountainous environments, using the Province of South Tyrol, Italy, as a model region. A Geographic Information System (GIS) framework was developed to identify suitable sites for vertical drone ports based on terrain, infrastructure, and regulatory constraints. A Low-Altitude-Flight Elevation Model (LAFEM) was implemented to generate obstacle-avoiding, regulation-compliant 3D flight paths using least-cost path analysis. The results identified 542 potential vertical-port locations, covering approximately 49% of South Tyrol within ten minutes of flight, and demonstrated significant time savings for AED delivery in field tests compared with manual and Euclidean routing. These findings show that integrating GIS-based vertical-port placement and terrain-adaptive UAV routing can substantially improve AED accessibility and response times in mountainous regions. The LAFEM model aligns with U-space airspace regulations and supports safe, automated AED deployment for improved outcomes in OHCA emergencies. Full article

The OrgTRx platform is a system designed in Queensland, Australia, for the capture of Antimicrobial Resistance (AMR) surveillance data. The data is captured directly from Microbiology Laboratory Information Systems. The most common use of this data is to create facility-specific antibiograms for hospitals and other healthcare facilities. We report on the methods adopted to extract susceptibility results from participating pathology services for AMR surveillance across Australia. OrgTRx receives standardised extracts of antimicrobial susceptibility data from laboratory information systems. This data is validated, verified and incorporated into a database each month. For visualisation by clinical users, the data is displayed in a data cube. The data that is received in this standardised format can be used to review trends in resistance by organism and geographical location of patients presenting with a wide range of infections across Australia. This information can be used to identify areas that require additional resources to combat AMR. The OrgTRx data cube provides clinicians with the tools to create facility-specific antibiograms as well as monitor trends in resistance in pathogens of interest. Increased laboratory capacity and capability, along with adequate funding of surveillance systems, will provide high-quality information to inform the implementation of strategies to prevent the spread of AMR. Full article

With the proliferation of mobile internet and location-based services, location-based social networks (LBSNs) have accumulated extensive user check-in data, driving the advancement of next Point-of-Interest (POI) recommendation systems. Although existing approaches can model sequential dependencies and spatio-temporal patterns, they often fail to fully capture users’ dynamic preferences under varying spatio-temporal contexts and lack effective integration of fine-grained semantic information. To address these limitations, this paper proposes Language-Guided Spatio-Temporal Context Learning for Next POI Recommendation (LSCNP). It employs a pre-trained BERT model to encode multi-dimensional spatio-temporal context—including geographic coordinates, visiting hours, and surrounding POI categories—into structured textual sequences for semantic understanding; constructs dual-graph structures to model spatial constraints and user transition patterns; and introduces a contrastive learning module to align spatio-temporal context with POI features, enhancing the discriminability of representations. A Transformer-based sequential encoder is adopted to capture long-range dependencies, while a neural matrix factorization decoder generates final recommendations. Experiments on three real-world LBSN datasets demonstrate that LSCNP consistently outperforms state-of-the-art baselines. Ablation studies and hyperparameter analyses further validate the contribution of each component to the overall performance. Full article

The worldwide transition to renewable energy systems is motivated by diminishing fossil fuel availability and the intensifying consequences of climate change. This study presents a Mixed-Integer Linear Programming (MILP) model for designing and optimising the bio-fuel and electricity supply chain in Colombia, using sugarcane as the main feedstock and integrating microalgae cultivation in vinasse. Six alternative biorefinery configurations and four microalgae conversion pathways were evaluated to inform strategic planning. The optimisation results indicate that microalgae achieve higher energy yields per unit of land than sugarcane. Ethanol production from sugarcane could meet all of Colombia’s gasoline demand, while diesel and sustainable aviation fuel derived from microalgae could supply around 9% and 16%, respectively, of the country’s consumption. Further-more, pelletised bagasse emerges as a viable alternative to replace part of the coal used in thermoelectric plants. From an economic perspective, all scenarios achieve a positive net present value, confirming their profitability. Sensitivity analysis highlights the critical factors influencing the deployment of distilleries as ethanol price, algae productivity, and sugarcane cost. Furthermore, transportation costs play a decisive role in the geographic location of microalgae-based facilities and the distribution of their products. Full article