Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.

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An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin\'s flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton\'s and Strahler\'s methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study\'s flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region\'s most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union\'s main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes.

Bangladesh has witnessed alarmingly rising lightning frequency, particularly during pre-monsoon and monsoon seasons. This has resulted in significant annual death tolls from lightning strikes over the past decade. Recognizing this crisis, the country officially declared lightning casualties a natural disaster in 2016. This study delves deeper into the landscape of lightning fatalities and causalities in Bangladesh. Utilizing secondary data sources, this research introduces a unique approach by integrating Bangladesh Meteorological Department (BMD) data and NASA\'s Lightning Imaging Sensor (LIS) data from the International Space Station\'s (ISS) Near-real Time (NRT) mission. This combined dataset allows for a more comprehensive analysis. Furthermore, Geographic Information Systems (GIS) was employed to analyze spatial distributions and generate maps. The Inverse Distance Weighted (IDW) interpolation tool was used to create detailed spatial distribution maps of lightning fatalities, thunderstorm days (TSDs), and lightning flash frequency (LFF) across Bangladesh. The analysis revealed that farmers and fishermen were the most vulnerable populations, with the northeastern regions experiencing the highest impact. Sylhet division emerged as the area with the most fatalities, highlighting the northeastern zone\'s susceptibility. The study also identified monsoons as the period with the highest occurrences of lightning deaths and injuries. By combining innovative data integration and spatial analysis, this study offers valuable insights into the alarming trend of lightning fatalities in Bangladesh. These findings can inform targeted prevention strategies and interventions to safeguard vulnerable populations and communities.

The migratory locust, Locustamigratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L.migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust\'s distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L.migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species\' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.

In the Irbid Governorate, Jordan, equitable healthcare facility distribution is vital to ensuring healthcare accessibility and improving public health outcomes. This study investigated the spatial distribution, accessibility, and conformity of healthcare facilities to the Ministry of Health standards to identify areas requiring improvement. Using geographic information systems (GIS), three spatial analyses were conducted: nearest neighbor analysis, buffer analysis, and service area analysis. These analyses comprehensively assessed the healthcare landscape, revealing a random spatial distribution pattern of healthcare facilities; and indicating an absence of structured organization. The buffer analysis revealed concentrations in specific regions, while others were underserved. The Service Area Analysis revealed significant healthcare access challenges, especially in remote areas. The healthcare resource distribution of the Irbid governorate fell short of national and international standards, emphasizing the need for improvements. To address these disparities, policymakers and healthcare authorities should focus on equitably redistributing resources, tailoring allocation to local needs, improving remote area infrastructure, and refining government policies. Continuous monitoring and evaluation are imperative to ensure alignment with international standards and achieve healthcare equity. The insights from this case study provide valuable guidance for regions facing similar healthcare distribution challenges.

Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today\'s densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.

A steep increase of small papillary thyroid cancers (sPTCs) has been observed globally. A major risk factor for developing PTC is ionizing radiation. The aim of this study is to investigate the spatial distribution of sPTC in Sweden and the extent to which prevalence is correlated to gamma radiation levels (Caesium-137 (Cs-137), Thorium-232 (Th-232), Uranium-238 (U-238) and Potassium-40 (K-40)) using multiple geospatial and geostatistical methods. The prevalence of metastatic sPTC was associated with significantly higher levels of Gamma radiation from Th-232, U-238 and K-40. The association is, however, inconsistent and the prevalence is higher in densely populated areas. The results clearly indicate that sPTC has causative factors that are neither evenly distributed among the population, nor geographically, calling for further studies with bigger cohorts. Environmental factors are believed to play a major role in the pathogenesis of the disease.