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This research seeks to advance the technological process of 3D digitization in built environments and streamline management processes in the construction sector through digital methodologies. To this end, an integration framework is proposed that combines geographic information systems (GISs) and building information modeling (BIM) digital models, specifically for simulating building facilities maintenance management. Although the proposed methodology is applicable across various geographical contexts and building typologies, to ensure clarity in its development, it was applied to a specific case study. For this purpose, a 3D GIS model was created for one of the campuses of the University of Cantabria in Santander, Spain, along with a BIM model for one of its university buildings. Using these integrated models, facility management was simulated within a 3D environment via a computerized maintenance management system (CMMS). The findings indicated that GIS and BIM digital models could indeed be integrated through straightforward linking mechanisms without compromising the efficiency of information synchronization and management. When comparing 2D facility management approaches with 3D formats, the advantages of 3D visualization became clear. This three-dimensional representation allowed for a more intuitive understanding of spatial dynamics and interactions, facilitating quicker identification of potential issues and more efficient maintenance operations. Consequently, integrating these advanced digital models not only optimizes operational efficiency but also fosters a collaborative environment, fundamentally transforming building facilities management. [ABSTRACT FROM AUTHOR]

The coastal Patuakhali district in Bangladesh is highly susceptible to catastrophic flooding due to its geophysical location. Saving the coastal settlements from these frequent and extreme flooding events is challenging. Thus, there is an urgent need for a detailed flood vulnerability, risk, and capacity assessment study in this area. This study attempted to provide a comprehensive assessment and mapping of coastal flood risk, identifying the most flood-vulnerable regions of the Patuakhali district to achieve efficient flood mitigation strategies. To generate a complete coastal flood risk scenario, this study simultaneously applied people's perception-based risk assessment using the MCDA, AHP, GIS, and RS-based advanced methodologies. Based on the combined judgments of both people's perceptions and experiences, GIS- and RS-based mapping, this study predicted a high flood risk for the studied area. Firstly, from people's perception and expert opinions-based approach, this study estimated a high flood risk (0.886) for the coastal Patuakhali district. Secondly, individual hazard scores of 0.765 and vulnerability scores of 0.644 were obtained during this assessment, which seems reasonable. However, their combined results produced a significant flood risk due to the lower community capacity (0.556) to withstand flood damage. Finally, a flood risk map was developed with the weighted overlay tool, considering ten flood-causing factors throughout the mapping process. Findings showed the majority of Patuakhali district's central, north-central, most southern, and south-eastern char and island area as being in a very high flood risk zone due to its high population density, development of in-built infrastructure, lower elevation, flat topography, and lack of vegetation cover. This information is supposed to support the relevant authorities and decision-makers. [ABSTRACT FROM AUTHOR]

Groundwater depletion due to overextraction poses a significant challenge in arid and semiarid rural regions, leading to heightened vulnerability to water scarcity. A comprehensive study of groundwater potential is crucial to employing effective and efficient technologies to address this issue. In this research, groundwater potential zones (GPZs) were generated for the semiarid Anantapur district using an integrated approach of a geographic information system (GIS) and multicriteria decision analysis (MCDA). Three MCDA models—analytical hierarchical process (AHP), best worst method (BWM), and full consistency M-method (FUCOM)—were applied to obtain GPZs by considering 20 groundwater influencing factors for a realistic analysis. A relationship assessment between variables and consistency tests were conducted before evaluating the GPZs to ensure high prediction accuracy. Subsequently, the GPZs were evaluated in the GIS environment using efficient MCDA techniques. The highest prediction accuracy was achieved by FUCOM at 80.6%, followed by BWM at 70.4% and AHP at 63.0%. The best of the three models, FUCOM, reveals that only 0.10% of the district was classified as having high GPZs, whereas 65.49% and 34.41% were classified as medium and low potential areas, respectively. For a better understanding of the contribution of various topographical, hydrological, geological, land use, and proximity parameters toward groundwater potential, map removal and single parameter sensitivity analysis were performed. The results highlighted the efficiency and reliability of geospatial modeling using MCDA techniques when evaluating groundwater potential and understanding the influence of various parameters. This study emphasizes the importance of employing GIS and MCDA methods for assessing groundwater potential in water scarcity regions. The findings provide valuable insights for sustainable groundwater management and resource planning in arid and semiarid regions. [ABSTRACT FROM AUTHOR]

This study supports Jamaica's renewable energy implementation strategies by providing updated wind atlases and identifying suitable locations for future wind farms. Using a GIS-based Analytic Hierarchy Process with multi-criteria decision-making (AHP-MCDM), this research integrates stakeholders' opinions, environmental considerations, and technical factors to assess land suitability for wind energy development. The analysis reveals that Jamaica has the potential to increase its wind power output by 8.99% compared to the current production of 99 MW. This expansion could significantly contribute to offsetting fossil fuel-based energy consumption and reducing carbon dioxide emissions. It identifies sites across several parishes, including Westmoreland, Clarendon, St. Mary, and St. James, as highly suitable for utility-scale wind farm development. By providing detailed spatial information and estimated energy outputs, this research offers valuable insights for energy planners, investors, and policymakers to create sustainable energy policies and advance Jamaica's 50% renewable energy goal by 2030. [ABSTRACT FROM AUTHOR]

In today's competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company's stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented. [ABSTRACT FROM AUTHOR]

Flood risk mapping is instrumental in guiding land-use decisions, development planning, disaster management, and mitigation strategies. However, the accuracy of such maps relies heavily on the availability of comprehensive data. When such data are lacking, empirical approaches are employed to estimate flood risk. Several recent studies have developed flood risk maps using multicriteria decision-making (MCDM), such as the analytical hierarchy process (AHP). However, flood risk mapping methods using MCDM techniques are zero-dimensional models, and they cannot be associated with a flood of a particular exceedance probability. Notably, flood inundation models can predict floods and help map flood parameters for floods with different return periods. Accordingly, this study proposes a new framework for mapping flood risk for floods with different return periods by integrating inundation maps obtained from a flood simulation model with an MCDM framework in a geographic information system (GIS) environment. The proposed method integrates remote sensing data, hydraulic modeling, and AHP combined with a sensitivity analysis to develop a flood risk map. The applicability of the proposed framework is demonstrated by employing it to create flood risk maps for flood events with different return periods in the East Fork White River (EFWR) in Columbus, Indiana, USA. The results reveal a significant correspondence between high-risk zones identified in the flood risk maps and areas with high values on an available flood damage map of the study area, confirming the efficacy of the proposed framework. This study highlights the potential of the methodology as a valuable tool for generating flood risk maps in areas where comprehensive flood risk assessment data are limited. Additionally, the flexibility of the GIS-based approach allows for the adaptation and application of the methodology to different geographic locations and flood scenarios. Thus, the proposed framework offers a robust and practical approach to flood risk mapping with potential applications in disaster management and land-use planning strategies. [ABSTRACT FROM AUTHOR]

This study was designed to reveal the possible effects of climate change on the landscape structure of the Bartın Stream Basin. Remote sensing (RS) and Geographic Information Systems (GIS) tools and statistical methods were employed throughout the study. Landsat satellite images, which are 30 m × 30 m resolution images produced by Landsat 4–5, Landsat 7, and Landsat 8-Oli satellites, were used. In addition, 42 variables were produced, including 19 bioclimatic variables, plant index data from satellite images, and environmental variables. The effect of the produced variables on land use-land cover (LULC) was investigated. Then, the expected situation in 2050 according to the RCP climate change scenarios was estimated using the R Studio software with time series analysis. The data for 2050 were modeled and mapped using the Maximum Entropy method. As a result, it was revealed that LULC changes within the basin would be in the form of artificialization and increased fragmentation, that bare lands and residential areas would increase, and that agricultural areas and forest areas would decrease by approximately 50%. Planning should be made in order to reduce the breakdown of landscape resistance by predicting the adverse events to be experienced due to climate change in the future. It was concluded that agriculture, which was determined as the development strategy of the region in the current Environmental Plan (EP) of the basin, would not be possible due to the approximately 50% loss in agricultural areas. This study revealed that the effects of climate change, which is the biggest threat of the age, could be revealed with statistical models. [ABSTRACT FROM AUTHOR]

The article focuses on analyzing the effects of Digital Elevation Model (DEM) characteristics, specifically resolution and preprocessing, on the Topographic Wetness Index (TWI) within the Painganga Wildlife Sanctuary in India. It highlights how variations in DEM quality influence hydrological modeling outcomes, spatial wetness patterns, and ecosystem management decisions.

As a city located in the northern coast of Central Java, Semarang continuously faces serious challenges related to annual tidal flooding. This research specifically examines the multi-temporal distribution of tidal floods in the 2020-2024 time span. Using the Change Detection method on Sentinel-1 SAR satellite imagery, the distribution of tidal flooding during this period was determined. The data obtained was then further processed with overlay techniques to produce more detailed information. This technique allows for mapping areas with different levels of tidal flood vulnerability, and the overlay results were used as one of the parameters in the Composite Mapping Analysis (CMA) method, which integrates various related parameters. The results point out that the coastal areas of Semarang City, including the sub-districts of Tugu, West Semarang, North Semarang, East Semarang, Gayamsari, and Genuk, are dominated by high vulnerability levels. These areas have a high vulnerability percentage of 40%, covering an area of 4,988.65 hectares. Medium vulnerability includes 33% of the area with 4,080.01 hectares, while low vulnerability covers 23% with 2,802.22 hectares. Areas with very high vulnerability only incorporates 3.9% with an area of 481.40 hectares, and the lowest is very low vulnerability at 0.1% with an area of 10.39 hectares. The sub-districts most affected by high vulnerability are Genuk, Tugu, West Semarang, North Semarang, Gayamsari, and East Semarang in succession. [ABSTRACT FROM AUTHOR]

The Lower Teesta River basin in northern Bangladesh, which is prone to recurrent annual flooding, requires refined flood susceptibility analysis. In this context, this paper presents a novel approach for quantifying flood susceptibility in the Lower Teesta River Basin through the Analytical hierarchy process (AHP) and Multi-criteria decision analysis (MCDA) using geographic information system (GIS) and remote sensing techniques. Ten flood conditioning parameters were selected for flood susceptibility modelling, based on a comprehensive literature review and expert opinion. These parameters include the topographic wetness index (TWI), elevation, slope, rainfall, proximity to rivers and roads, soil type, drainage density, land use and land cover type, and the normalised difference vegetation index (NDVI). This comprehensive assessment resulted in the creation of an exact flood risk map that delineates areas within the basin that are prone to high or very high flood risk. The model was subjected to rigorous validation using ROC-AUC analysis, yielding an impressive AUC score of 0.848, underscoring its robust predictive capabilities. These results highlight the importance of continued monitoring and targeted flood risk reduction measures in this region, particularly in high-risk areas. Although the study was conducted on a regional scale, it has a broad readership appeal and impact that extends beyond the region. The approach employed in this study is transferable to a range of environmental settings, both within Bangladesh and globally. [ABSTRACT FROM AUTHOR]

This study focuses on the Pteroptyx tener species in the Sepetang River, Malaysia, aiming to evaluate the firefly's abundance and explore its correlation with various biotic and abiotic parameters. The study was conducted over six months, from November 2021 to April 2022, utilizing Geographic Information System (GIS) software to apply hotspot mapping and Inverse Distance Weighting (IDW) analysis to elucidate the spatial distribution of firefly populations. A total of 111,615 individuals were recorded, with a particular focus on this firefly species' presence on their display trees. Hotspot analysis showed that Station 6, located at the mouth of a river with dense mangroves, hosted 55,723 fireflies (50.01%). In contrast, Stations 9 and 10, near ponds and shrimp settlements, recorded 517-723 fireflies (0.65% and 0.46%). Pearson's correlation coefficient (r) unveiled a statistically significant positive correlation (r = 0.88, p < 0.05) between wind speed and the abundance of firefly populations within the Sepetang River. However, no statistically significant correlation (p > 0.05) was found between firefly abundance and various other abiotic parameters, including relative humidity (RH), air temperature, tide level, pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), and water clarity. Thus, the results revealed the preference for fireflies due to the availability of vegetation, wind speed and minimal disturbance in this area. In conclusion, this study's information significantly adds to our understanding of these interesting insects and their complicated relationships in nature. It underscores the importance of preserving their habitats and ecosystems. [ABSTRACT FROM AUTHOR]

Groundwater serves as a crucial water source for diverse human activities in arid and semi-arid areas, particularly in the Zagora region. The present research aims to assess the physicochemical parameters and their spatial distribution in the groundwater of the study region. The water quality data from nine designated wells were monitored during December 2020 and December 2021 in the Zagora region. Water samples from various wells in the Zagora region were examined for a range of physicochemical parameters. Twelve parameters related to groundwater quality, including chloride (Cl−), dissolved oxygen (O2), bicarbonate (HCO3−), sulphate (SO42-), nitrate (NO3−), nitrite (NO2−), calcium (Ca2+), magnesium (Mg2+), hydrogen ion concentration (pH), electrical conductivity (EC), sodium (Na+) and potassium (K+) were subjected to analysis. ArcGIS software was utilized to generate a themed map that illustrates the variability of the Water Quality Index (WQI) using a Geographic Information System (GIS). The WQI map demonstrated that the groundwater quality is poorer in the areas upstream. With 44.44% of groundwater tests in this area falling into the unfit for drinking category, it is not appropriate for human consumption. Moreover, water quality variations highlight anthropogenic impacts, emphasizing the need for effective management to mitigate groundwater contamination and pollution.

Photogrammetric and remote sensing studies have a wide range of applications. They are popular data sources for various purposes, including spatial analysis, site and object surveys, and environmental studies. Research aimed at developing an optimal and reliable erosion model requires a specialized and comprehensive approach due to the numerous natural and anthropogenic factors that must be considered. Physical and chemical characteristics of soils change according to land-use practices, terrain topography, and prevailing meteorological conditions. Such diverse issues require detailed and specific investigations. Data for these studies can be obtained through spatial analyses based on remote sensing and photogrammetric data as well as thematic cartographic studies. We present a method for acquiring and processing various types of geospatial data using geographic information system (GIS) tools to generate the individual parameters required in the G2 erosion model. This article was conducted in an area covering 36.3 km2, corresponding to the Odra River watershed in the Silesian Voivodeship, Poland. This article employed publicly available high-resolution thematic layers, such as high-resolution layers from the Copernicus Land Monitoring Service and Sentinel-2. Methods such as remote sensing, GIS analysis, and normalization of available parameters were used to determine various parameters of the G2 erosion model. This effort yielded a high-resolution erosion map, facilitating the accurate determination of the model parameters at any given location on the site.

The article presents the results of an epidemiological study of the incidence of rabies in the population of the Republic of Cameroon in the period from 2014 to 2022. The system of epidemiological surveillance and monitoring of rabies in Cameroon is presented. According to WHO, human and animal rabies is recognized as endemic to Central Africa as a whole, and in Cameroon, in particular, this infectious goiter is classified as the first priority zoonosis within the framework of the National Program for the Prevention and Control of Recurrent and Re-emerging Zoonosis (PNPLZER). The average annual incidence of rabies recorded between 2014 and 2022 was 405.7% (95% CI: 401.9% – 409.5%). Based on the results of a retrospective epidemiological analysis, the dynamics of rabies cases among people in Cameroon is presented in accordance with data recorded at the Operational Center for Public Health Emergencies (CCOUSP). The highest rates were recorded in the period from 2018 (547.4%) to 2019 (276.8%), and the lowest in 2021 (10.7%) to 2022 (3.1%). To identify socio-demographic risk factors, a survey of rabies foci was conducted using specially designed questionnaires (epidemiological information collection cards). Mapping of registered cases of rabies in humans was carried out for 10 administrative regions of the country using the LTR QGIS (quantum GIS) program, which allowed us to show the distribution of cases across the country and dynamics over time. It has been established that the distribution of cases of the disease across the territory does not always depend on the population density in administrative districts, but is associated with specific socio-demographic risk factors such as profession, gender, age, type of animal reservoir of the virus, which affect the activity of the pathogen circulation among natural hosts.

The development of Geographic Information System (GIS) technology has provided significant benefits in various fields, including the monitoring of crime-prone areas. GIS is used to minimize the traces of these crimes. This study aims to map crime-prone areas in the Samarinda Seberang and Loa Janan Ilir Districts to identify which areas are potentially vulnerable, allowing for analysis for prevention and handling. The data used were collected from theft cases that occurred in these districts in 2019 and 2020. The research employs a scoring technique where each parameter is rated according to its classification. The results of the scoring process are then analyzed to determine the level of crime-prone areas, categorizing them as very vulnerable, vulnerable, or not vulnerable. Based on respondents' feedback, the application facilitates users in locating crime-prone areas, with 94.34% of responses indicating agreement or strong agreement. These results suggest that the application is feasible for implementation.

Highlights: • The study introduces novel methods in spatial analysis to reinterpret long-standing archaeological theories about settlement distribution • Spatial analysis reveals fluctuating proximity of Bronze Age settlements to the coast in the Saronic Gulf, influenced by socio-cultural and climatic changes. • Shifts in settlement patterns and external factors like the rise of Argolic centers reshaped Kolonna's influence, reorienting it towards its hinterlands. Abstract: This study explores the interrelation between settlement dynamics and coastal proximity during the Bronze Age in the Saronic Gulf, utilising an innovative spatial analytical approach. By integrating Geographic Information System (GIS) and statistical methods in R, this paper analyses a dataset comprising 258 archaeological sites across diverse coastal and inland environments. The methodology uses the Movecost package for R to calculate least-cost paths, quantifying the ease of access to coastlines, and enabling a nuanced interpretation of settlement patterns over time. Results indicate significant shifts in settlement patterns linked to socio-economic, climatic, and political changes. The early phases, particularly during Early Helladic II, show an increased distance from the coast, suggesting a period less reliant on maritime activities despite the existence of extensive maritime networks. Conversely, Early Helladic III and Middle Helladic III–Late Helladic II periods mark a more pronounced coastal orientation; in the first case, it was probably connected to climatic instability and survival strategies and, in the second one, connected to socio-political change and economic opportunities. The analysis challenges traditional views of constant coastal habitation. Instead, it reveals a complex pattern where coastal proximity was not solely dictated by maritime capabilities: it was a strategic choice influenced by a myriad of factors, including security, agricultural potential, external trade relations and climatic change. The rise and fall of Kolonna, a significant urban centre, underscores these dynamics, as shifts in its regional influence correlate with broader Aegean power structures and climatic events. This paper contributes to the understanding of how ancient societies adapted their settlement strategies in response to changing socio-political circumstances. It also demonstrates the potential of R and spatial statistics as powerful tools for archaeological inquiry, providing new perspectives on traditional interpretations of ancient settlement patterns.

In recent times, increased geogenic and human-centric activities have caused significant heavy metal(loid) (HM) contamination of soil, adversely impacting environmental, plant, and human health. Phytoremediation is an evolving, cost-effective, environment-friendly, in situ technology that employs indigenous/exotic plant species as natural purifiers to remove toxic HM(s) from deteriorated ambient soil. Interestingly, the plant's rhizomicrobiome is pivotal in promoting overall plant nutrition, health, and phytoremediation. Certain secondary metabolites produced by plant growth-promoting rhizobacteria (PGPR) directly participate in HM bioremediation through chelation/mobilization/sequestration/bioadsorption/bioaccumulation, thus altering metal(loid) bioavailability for their uptake, accumulation, and translocation by plants. Moreover, the metallotolerance of the PGPR and the host plant is another critical factor for the successful phytoremediation of metal(loid)-polluted soil. Among the phytotechniques available for HM remediation, phytoextraction/phytoaccumulation (HM mobilization, uptake, and accumulation within the different plant tissues) and phytosequestration/phytostabilization (HM immobilization within the soil) have gained momentum in recent years. Natural metal(loid)-hyperaccumulating plants have the potential to assimilate increased levels of metal(loid)s, and several such species have already been identified as potential candidates for HM phytoremediation. Furthermore, the development of transgenic rhizobacterial and/or plant strains with enhanced environmental adaptability and metal(loid) uptake ability using genetic engineering might open new avenues in PGPR-assisted phytoremediation technologies. With the use of the Geographic Information System (GIS) for identifying metal(loid)-impacted lands and an appropriate combination of normal/transgenic (hyper)accumulator plant(s) and rhizobacterial inoculant(s), it is possible to develop efficient integrated phytobial remediation strategies in boosting the clean-up process over vast regions of HM-contaminated sites and eventually restore ecosystem health. [ABSTRACT FROM AUTHOR]

Accurate signal path loss models for predictions are crucial in current cellular communication networks. Recently, numerous path loss estimation methods have been presented to improve the efficiency of networks. However, most of these existing models do not include spatial data such as land use/land cover, terrain elevation, building height, and the effect of topography. To address this issue, this study proposes a GeoAI-based technique for path loss estimation in cellular communication networks, addressing existing models' lack of spatial data integration. Support Vector Regression, K-Nearest Neighbor, Random Forest, and multi-layer perceptron (MLP) artificial neural network models are evaluated using field measurements in an urban, suburban area in Van, Turkey, across various frequencies. Among the models, MLP with three hidden layers, nine input variables, hyperbolic tangent activation function, and Adam optimization method performs best. At 900 MHz, MLP has been observed with MSE, RMSE, MAE, and R values of 0.22 dB, 0.47 dB, 0.46 dB, and 0.99 dB, respectively. Lastly, a comparison of the developed model to the Free space, COST 231, Ericsson, and SUI models revealed that the GeoAI-based path loss models outperformed the empirical models regarding prediction accuracy and generalization. This study underscores the significance of integrating spatial data into path loss prediction, particularly in diverse urban and suburban environments, for optimizing cellular communication networks. [ABSTRACT FROM AUTHOR]

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. [ABSTRACT FROM AUTHOR]

Background: Emergency Medical Services (EMS) is the first point of service for people who are in critical conditions and need emergency services, and high blood pressure is one of the most important causes of death in Iran and the world. Materials and Methods: This applied and descriptive-analytical research, using spatial statistics and geographic information system (GIS), analyzed the epidemiologic space-time of blood pressure in the city of Mashhad. The statistical population, the total number of blood pressure patients (3555 people), are within the legal limits of the city of Mashhad, who contacted the EMS center between 2018 and 2019. To identify the spatial pattern of blood pressure disease, in the analysis of the spatial distribution pattern of central complication techniques, standard deviation curve, high/low clustering analysis, hot spot analysis, global and local Moran index and kernel density analysis to analyze and how the pattern Spatial distribution of blood pressure disease has been used by geographic information system. Results: The findings of the research show that the index of clustering analysis in the spatial pattern is in the form of severe clustering. Cornell's density estimation model in the period of 2018-2019 showed that the most areas involved in blood pressure diseases include the northeastern areas towards the center of Mashhad city, where patients with high or low blood pressure have gathered in hot or cold clusters. Conclusion: By using the analytical results of this research, a comprehensive understanding of the centers of blood pressure disease, and the spatial patterns and geographical distribution of health, in Mashhad city, has been achieved, which can be taken into account in preventive measures. [ABSTRACT FROM AUTHOR]

This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of interest (POI) data, analyzed with ArcGIS and a decision tree model. The results indicated that persons aged 60–65 accounted for 42.8% of the total sample and primarily chose to migrate in the early stages of retirement. The intra-city migration of older adults exhibits both centripetal and centrifugal patterns, with a greater tendency toward the city center. House prices, floor levels, and commercial facilities significantly impact their choice of migration destinations. Among these, house prices were the most critical determinant, with the majority of older adults migrating to neighborhoods with lower house prices. This study contributes by integrating residential migration and location choice research and constructing an analytical framework based on facility accessibility. The findings provide insights into the key determinants of location choice for intra-city residential migration among older adults and the construction of livable neighborhoods for them. [ABSTRACT FROM AUTHOR]

This article discusses the application of gis in the study of military history, particularly for campaigns in modern Asian history (1800s-1950s), citing the Battle of Hong Kong 1941 Spatial History Project as an example. gis allows researchers to move beyond text-based narratives by visualizing, contextualizing, and explaining the diversity of human experiences that are often overshadowed by frontline actions. This article assesses the use of gis-based interactive maps in visualizing the flow of the battle, reconstructing the battlefield, examining the diverse human experience, and integrating disparate historical data. Reflecting on the research team’s interdisciplinary experience in creating a gis-based interactive map, this article argues that such a method is suitable for military campaigns that took place in a rapidly changing urban environment. It also discusses the limitations of this method and the need to remain critical towards the visualization of data, the use of sources, and interpretation. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) changes hugely influence the ecological balance of an ecosystem, which adversely affects the inhabitants, making them more vulnerable to natural calamities. The LULC change studies are therefore carried out to analyze the impact of these changes on the overall ecology of an area and are very helpful in policy framing and proper management of the available natural resources. In this study, changes in the land use and land cover for a three-decade period spanning from 1992 to 2020 have been monitored in the valley of Kashmir using remotely sensed satellite data obtained from USGS/NASA’s Landsat repository. Considerable changes in the LULC patterns were observed with a significant reduction in the area covered by water (18.21%), forest (13.56%), snow/glacial cover (29.32%) and agriculture (22.37%) during the past three decades. Concurrently, expansion in the land covered by urban areas (22.33%), barren land (37.32%), plantation (14.53%) and marshes (13.21%) were noted. The calculated Normalized Difference Water Index (NDWI) confirmed an overall reduction of 51.1% in the water and glacial cover of the study area. Significant changes in the form of forest, water and glacial cover transforming into urban, marshy and barren areas can be largely accredited to increased human interference that may have serious repercussions on the environment. [ABSTRACT FROM AUTHOR]

A geographic information system (GIS) is a tool and technology capable of addressing the effects and challenges of natural disasters, particularly flash floods. GIS applications are used to generate flood risk maps to tackle flood issues. However, various challenges and problems arise when employing GIS to manage flash flood disasters in Shah Alam, Malaysia. Hence, this study aims to identify these challenges and gaps in GIS utilisation by Malaysian agencies for flash flood management in Shah Alam. Using the quadruple helix model technique, informal interviews were conducted as part of the study's qualitative methodology. Five respondents were chosen from each of the four main sectors for primary data collection: government, academia, business, and community/NGO. The data were analysed using Taguette qualitative theme analysis. The findings reveal that the primary challenges lie in government management, particularly in providing equipment and access to GIS for all stakeholders, including the public. This challenge is attributed to the high costs and complexity associated with GIS data usage, limiting accessibility. Furthermore, there is a lack of expertise and research on GIS in Malaysian universities concerning flash flood management. The government should take proactive steps to improve flash flood management in Shah Alam, Malaysia, in order to solve these issues. Specifically, GIS training should be given to stakeholders, particularly those in the government and academic sectors, in order to develop GIS specialists who will be necessary for efficient flood management in Malaysia. [ABSTRACT FROM AUTHOR]

Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors during sub-index computation. To overcome this limitation, it becomes critical, therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. Therefore, in this study, the overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 29.2% of the area is suitable for irrigation (low restriction), 41.7% is moderately suitable (moderate restriction); and 29.1% is unsuitable (high restriction), with the irrigation water quality declining towards the central-southeastern direction. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl−, HCO3− SO42−, NO3−, Ca2+, Mg2+, Na+, K+, pH, EC, TH, and TDS as input variables, and MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high-performance accuracy based on the results of the coefficient of determination (R2 = 0.513–0.983). Low modeling errors were observed from the results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R2adj), and residual plots, further confirming the efficacy of the two models; although the MLP-ANNs showed higher prediction accuracy for R2. Based on the sensitivity analysis of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and machine learning can serve as rapid decision-making tools for proper planning and enhanced agricultural productivity. [ABSTRACT FROM AUTHOR]

Groundwater is a vital natural resource and has an important role in the economy. It is the main source of water for irrigation and food industry. In general, groundwater is a reliable water source for agriculture and can be used flexibly during dry periods. Moreover, the use of geographic information systems (GIS) has shown great effectiveness in the study of groundwater since they present a very essential and rapid result. It allows the establishment of thematic maps that are useful for future developments and to control the quality of groundwater. For this reason, the present study aims to delimit the potential of Wadi Mekerra groundwater basin, located in the North-Western part of Algeria, characterized by an arid and semi-arid climate. This aquifer, which extends over more than 2800 km2, sis unconfined, drained through Wadi Mekerra, and exploited by a fairly impressive number of wells and deep wells, almost the majority of which are used to irrigate agricultural land. In the current study, an analytical hierarchical process technique (AHP) was integrated with a geographic information system. A total of eight thematic layers were established and assessed for groundwater potential zone delineation, including geomorphology, geology, land use/cover, lineament density, drainage density, rainfall, soil and slope. All thematic maps' weights for each class are determined by the AHP approach based on each class's attributes and water potential capacity. Data from springs, wells, and deep wells and their chemical analyses were carefully used for validation. The map of the groundwater potential zone was, then, divided into five categories: very good, good, moderate, low, and very low. The study shows a very low and low groundwater potential zone that covers around 50.55% of the study area. The percentages of areas with very good and good groundwater potential are 4.15 and 11 percent, respectively. The moderate groundwater potential zone covers 59 % of the basin. [ABSTRACT FROM AUTHOR]

High fluoride concentrations in soil, water, or air can pose serious environmental and health risks to plants, and animals. Along with other hydrochemical parameters, this study investigates fluoride concentrations in the groundwater in the Ludhiana and Amritsar districts of Punjab, India. A total of 222 water samples were uniformly collected at approximately five-kilometer intervals for hydrochemical analyses. Statistical methods such as inverse distance weighting (IDW) and correlation matrices were used to assess the fluoride distribution and its relationships with other parameters. According to WHO guidelines, most fluoride concentrations were below 0.6 ppm in Ludhiana (84.30%) and Amritsar (77.23%). Fluoride levels that were within the permissible range (0.6-1.5 ppm) were found in 15.70% of Ludhiana's samples and 21.78% of Amritsar's samples; only 1% of Amritsar's samples exceeded the permissible limit (>1.5 ppm). The water quality index (WQI) analysis indicated that 0.83% of the groundwater samples from the Ludhiana district and 4.95% from the Amritsar district were unfit for consumption. This study demonstrates the importance of standardized sample collection and the use of GIS technology for comprehensive hydrochemical assessments, raising awareness and reducing health risks. [ABSTRACT FROM AUTHOR]

The morphometric indices (MI) have been calculated along the Gavilgarh/Salbardi Fault (G/SF) to investigate active deformation, tectonic uplift, and basin tilt to find out its impact on the late Quaternary landscape. To explore the tectonic intensity, we computed MI such as stream-length gradient index (SL), hypsometric integral (HI), transverse topographic symmetry factor (T), asymmetry factor (Af), basin elongation ratio (Re), mountain front sinuosity (Smf) and valley floor width to valley height ratio (Vf). The necessary field investigations were carried out on the evolution of the Quaternary landscape to ascertain the various morphometric indices. The evaluated values of SL, HI, Re, Smf, and Vf show the high uplift of the northern block exists as opposed to the southern block, and the tilt and asymmetric character of the basins are shown by T and Af values. The presence of sedimentary landscapes like Quaternary alluvium, triangular facets, natural levees, and boulder-pebbly beds, indicates that the area has recently experienced tectonic activity. The assessed values of MI with supporting field evidence proposed that the northern block is uplifted and the basins are tilted because of the effect of G/SF. [ABSTRACT FROM AUTHOR]

Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand‐driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location‐aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web‐based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F‐measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The lack of soil erosion data and other information about watersheds continues to limit soil erosion modeling. To overcome these limitations, many researchers have turned to machine learning models to analyze and model the complex water erosion processes and integrate them with empirical models. The Beni Haroun dam watershed faces soil erosion due to specific geo-environmental settings and land practices. It poses serious threats to agricultural and natural resource development. For these reasons, this study attempts to identify soil erosion susceptible zones using the Revised Universal Soil Loss Equation (RUSLE) using five key factors (rainfall erosivity, soil erodibility, topography, cover management and conservation practice factor) in GIS environment. Furthermore, we integrated the five RUSLE parameters and the model outputs into two machine learning (ML) algorithms, namely Random Forest (RF) and Random Tree (RT). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Our results indicated that the most vulnerable to severe soil erosion was concentrated in northwest regions, in contrast to the southeastern regions, which most occupy low erosion and moderate erosion. RUSLE and RT-based RUSLE models yielded nearly identical results in classifying erosion severity, estimating the annual average soil erosion at 17.5 and 17.69 (t ha–1y–1), respectively. In contrast, the Random Forest RF-based RUSLE model presented slightly divergent findings 23.89 (t ha–1y–1). Overall, these findings contribute to the identification of the area's most vulnerable to soil erosion, providing valuable insights to inform land management and conservation strategies. [ABSTRACT FROM AUTHOR]

The integration of urban agriculture into cityscapes necessitates a comprehensive understanding of multiple engineering and environmental factors, including urban fabric, building configurations, and dynamic energy and material flows. In contrast to rural settings, urban areas introduce complexities such as hygrothermal fluctuations, variable sunlight exposure and shadow patterns, diverse plant dimensions and shapes, and material interception. To address these challenges, this study presents an open-source Digital Twin model based on the use of a geographical information system (GIS) for near-real-time solar radiation mapping. This methodology aims to optimize crop productivity, enhance resilience, and promote environmental sustainability within urban areas and enables the near-time mapping of the salient features of different portions of the city using available open data. The work is structured into two main parts: (i) definition of the GIS-based Digital Twin model for mapping microclimatic variables (in particular solar radiation) to support sustainable urban agriculture design and (ii) application of the model to the city of Milan to verify its replicability and effectiveness. The key findings are connected to the possibility to integrate open data (solar radiation) with measurements in situ (illuminance and data referred to the specific crops, with related conversion coefficient) to develop a set of maps helpful for urban farmers but also for designers dealing with the synergy between buildings and urban farms. Initially tested on a neighborhood of Milan (Italy), the model will be applied in the Singapore context to verify analogies and differences. This correlation facilitates a more practical and straightforward examination of the relationships between solar irradiation and illuminance values of natural sunlight (involving both incident and diffuse light). The consistency of measurements allows for the precise documentation of these fluctuations, thereby enhancing the understanding of the influence of solar radiation on perceived luminance levels, particularly in urban environments characterized by diverse contextual factors such as vegetation, nearby structures, and geographical positioning. [ABSTRACT FROM AUTHOR]

Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events. [ABSTRACT FROM AUTHOR]

A tsunami is a sequence of powerful waves or surges primarily resulting from underwater earthquakes. Creating tsunami vulnerability maps is of utmost importance to develop effective strategies for mitigating potential damage caused by future tsunamis. This article focuses on areas (Indian subcontinent) severely affected by the 2004 Indian Ocean tsunami. Utilizing geographic information system (GIS)-based tools, the authors employed geospatial cell-based modeling and used a multicriteria decision-making tool, an analytical hierarchy process (AHP), to develop the final inundation map. Using the digital elevation model and Environmental Systems Research Institute (ESRI) land-cover data, the authors created the elevation, slope, coastal proximity, flow accumulation, and land-use land-cover (parameters) map. Areas with a higher risk of tsunami impact are predominantly situated along the coastline with a descending terrain. The presence of waterways and lower elevations intensifies the impact of tsunamis. Regions classified under very high or high vulnerability are more likely to be inundated by the tsunami. The final vulnerability map of Nagapattinam shows that 166.455 km2 of the region is highly vulnerable to a tsunami. Similarly, Kanyakumari, Cuddalore, Karaikal, and Chennai show 37.6516 km2 , 245.641 km2 , 35.7128 km2 , and 35.6306 km2 , respectively, under very high vulnerability. The findings of this study serve as fundamental information for disaster mitigation and urban planning in coastal regions. The research introduces a novel approach to assess areas susceptible to tsunami inundation, utilizing a vulnerability map generated through remote sensing and spatial multicriteria analysis. Furthermore, the parameters employed closely resemble those used in actual inundation mapping, adding to the practicality and reliability of the results. Village maps of the selected area are superimposed on the final vulnerability map to understand the villages vulnerable to tsunamis better. The final vulnerability maps can be used for strategic mitigation during an actual event and to be prepared for future disasters. [ABSTRACT FROM AUTHOR]

Land use/cover change(LUCC) has a significant impact on the ecological environment. Within the Belt and Road Initiative (BRI), as the largest cross-spatial cooperation initiative in human history, one of the core issues is how to scientifically and effectively use and manage the land in the region to prevent the destruction of important ecological and environmental resources. In order to reduce impact on the latter, in this study, we used the bivariate choropleth–multiple-criteria decision analysis (BC-MCDA) method based on the connotation of the sustainable development goals to construct an ecological and environmental risk warning framework. We found that in the study area, 10.51% of the land has high ecological and environmental risk and importance, corresponding to conflict zones, which require special attention. Conflict areas are mainly distributed in the Gangetic Plain in India, the plains in central and southern Cambodia, the Indonesian archipelago, and the southern coastal areas of China. Due to the uneven spatial distributions of population and important ecological and environmental resources, the pressure on this type of land use is very high. A share of 8.06% of the land has high risk–low importance, corresponding to economic development zones. Following years of human development, the ecological and environmental value of this type of land is low. A share of 58.75% of the land has low risk and importance, corresponding to wilderness areas. The natural climatic conditions of this type of land are relatively poor, often characterized by a cold climate or water scarcity, and the human interference index is low. A share of 22.68% of the land has low risk–high importance, corresponding to ecological conservation areas, which are the most important areas for ecological function services for humans at present. Finally, we proposed development suggestions for each type of land. [ABSTRACT FROM AUTHOR]

This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks. [ABSTRACT FROM AUTHOR]

The present research aims to select optimal areas for the installation of wind farms in the north-eastern part of Cuba. The research is based on the free software QGIS, Hierarchical Process Analysis, databases of the information of the alternatives and criteria and Geographic Information Systems (GIS), which makes it possible to know the places where these farms will be installed. In addition, experts were consulted to obtain the weights or level of importance of the selected criteria, such as distance to ports, distances to common roads, distances to population centers, to electrical grids and finally the wind speed. As a result, a mathematical conceptual model was obtained for the selection of optimal zones for the installation of wind farms in the eastern north of Cuba, by means of the MCDM and GIS methods, where it was determined that, within the evaluated alternatives of wind farms, Gibara III is the optimal one. These models will allow the development of wind projects in the eastern region of Holguin, and to be able to manage human, financial and material resources. [ABSTRACT FROM AUTHOR]

The ongoing demographic transition within aging single-family house neighborhoods in Germany poses a significant challenge for municipalities. The scarcity of data and information related to demographic composition and location quality complicates research efforts and the development of adaptive strategies for these residential areas. This issue is particularly pronounced in rural regions where resources for capturing and analyzing demographic trends are limited. To address this gap, we propose a methodology based on geographic information systems. In this approach, municipal population registers serve as a central data source for extracting insights about the residents. We present the findings primarily in the form of maps, as they are intended to be easily comprehensible for urban planners and local government staff. Additionally, we outline the initial steps in establishing a small-scale monitoring system that incorporates demographic indicators as well as reachability estimates. A case study from northern Bavaria is used as an illustration. [ABSTRACT FROM AUTHOR]

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Coastal areas are vulnerable to seawater intrusion. This research focused on the south coast of Jember Regency to analyze the distribution of salinity levels and suitability for agricultural irrigation. The research was a quantitative research, field survey method to analyze salinity level using parameter EC (Electrical Conductivity), and irrigation suitability using parameter (TDS, SAR, & %Na). Analyze the distribution of irrigation suitability samples using Wilcox and USSL diagrams, and visualized in maps using IDW interpolation. Salinity level is dominated by medium class with a total of 47 samples, and interpolation results are dominated by medium class with an area of 15,648 ha. Irrigation suitability based on the Wilcox diagram shows the dominance of samples in the good class totaling 7 samples, with interpolated %Na dominated by the excellent class with an area of 14,122 ha. Analysis of irrigation suitability based on USSL diagram is dominated by medium class (C3-S1) with a total of 7 samples, and SAR interpolation results are dominated by excellent class with an area of 26,209 ha. Analysis of irrigation suitability based on TDS parameters is dominated by the class none risk with a total of 57 samples, with interpolation results dominated by the class none risk with an area of 25,663 ha. Mapping provides an overview of salinity and irrigation suitability in the study area. These findings can be the basis for more efficient water management in coastal areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chin State in Myanmar experiences frequent landslides annually. This research aimed to construct GIS-based landslide susceptibility maps (LSMs) with two kinds of machine learning models, namely random forest (RF) and support vector machine (SVM). Firstly, a landslide inventory map was constructed by containing 213 landslide locations and randomly chosen 213 non-landslide locations; these location points were randomly divided into the training set (70 %) for the landslide susceptibility prediction model and the testing set (30 %) for the model validation. Secondly, twenty-one landslide conditioning factors were selected, and frequency ratio analysis was used to evaluate the relationship between each class of factors and landslide occurrences. Then, landslide susceptibility prediction modeling by RF and SVM models. Finally, the performance of the two models was evaluated with performance metrics (precision, recall, F1-Score, and accuracy), receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC values). The RF model demonstrated superior performance across performance evaluation metrics, with a precision of 0.864, recall of 0.919, F1-Score of 0.891, and an accuracy of 0.894 on the training set, compared to the SVM model's precision of 0.854, recall of 0.807, F1-Score of 0.830, and accuracy of 0.825. The model validation by the testing set further confirmed that the RF model showed a precision of 0.839, recall of 0.897, F1-Score of 0.867, and an accuracy of 0.871, while the SVM model had a precision of 0.839, recall of 0.839, F1-Score of 0.839, and an accuracy of 0.839. Also, the results of AUC values showed that the RF model (training set AUC = 0.94, testing set AUC = 0.92), and SVM model (training set AUC = 0.89, testing set AUC = 0.88), respectively. Hence, these two landslide susceptibility prediction models demonstrated satisfactory results and good accuracy for LSMs in this research area, and the LSM from the RF model is better than the SVM model according to performance metrics and AUC values results. The resulting maps provide useful information on the likelihood of landslide occurrence, facilitating decision-making in land use planning and disaster management. [ABSTRACT FROM AUTHOR]

Due to the numerous infrastructure development projects taking place in developing nations, large amounts of construction and demolition waste (C&DW) are unavoidable. Most C&DW management strategies, however, involve ‘isolated’ operations with high overhead. Waste data transfers are difficult to watch over. Important information needed for decision-making is, therefore, challenging to obtain. This paper’s goal is to discuss C&DW management from an integrative standpoint. Using GIS functions and a fuzzy analytical hierarchy approach, the best sites for waste management facilities can be found. A mixed integer optimization model is created to identify optimal location and allocation of waste management facilities. The objective is to reduce waste collection costs under different operational scenarios. By incorporating ‘constructs elements’ and ‘connected elements’ that are linked, isolated C&DW management ventures are transformed into successful C&DW management ventures. The effectiveness and efficiency of waste management strategies are evaluated using GIS analysis. A dashboard for tracking waste data flows is created to show how stakeholder decision making can be made easier. The proposed integrative approach differs from other approaches since it re-purposes location-allocation models and GIS data to optimize ‘isolated’ C&DW management practices. In addition, the notion of ‘construct elements’ that can be time-shared is outlined. Moreover, the notion of ‘connected elements’ that are dynamic, time-limited, and yet transient enough to provide the necessary interconnectivity is developed to support and complement the seamless function of a network of C&DW management facilities. The usefulness of the integrative approach is illustrated by a case study. Analysis of case studies demonstrates that waste management strategies in developing countries can be made more effective and waste collection efficiency can be increased. According to comparative studies, updating current waste management facilities can cut operating costs by 26%, while implementing an integrative strategy can cut costs by 52%. The discussions in this paper can help towns reevaluate C&DW management and put useful, unified C&DW management techniques into practice.

Mental disorders are a growing problem worldwide, putting pressure on healthcare systems and wider society. Anxiety and depression are estimated to cost the global economy US$1 trillion per year, yet only 2% of global median government healthcare expenditure goes towards mental health. There is growing evidence linking housing, socio-economic status and local environmental conditions with mental health inequalities. The aim of this paper is to link several open-access datasets at the local area level (N = 32,844) for England to clinical mental health metrics and describe initial statistical findings. Two mental health metrics were used: Small Area Mental Health Index (SAMHI) and diagnosed depression prevalence. To demonstrate the utility of the longitudinal mental health data, changes in depression prevalence were investigated over two study periods (2011–19, i.e. austerity; and 2019–22, i.e. COVID-19). These data were linked to housing data (energy efficiency, floor area, year built, type and tenure) from Energy Performance Certificates (EPCs); socio-demographic data (age, sex, income and education deprivation, household size) from administrative records; and local environment data (winter temperature, air pollution and access to green space). The linked dataset provides a useful resource with which to investigate the social and environmental determinants of mental health. Practice relevance Initial observations of the data revealed a non-linear relationship between home energy efficiency (EPC band) and the mental health metrics, with depression prevalence higher in local areas where the mode EPC bands were C and D, compared with B and E. Researchers can further investigate this relationship using the dataset through robust statistical analysis, adjusting for confounding variables. National and local governments may use the dataset to help allocate resources to prevent and treat mental health conditions. Practitioners can map and interrogate the data to describe their local areas and make preliminary conclusions about the relationships between the built environment and mental health. This preliminary analysis of the data demonstrated a gradient in SAMHI and depression prevalence with income and employment deprivation at the local area level.

In Malaysia, escalating human-wildlife conflicts pose significant risks to both human populations and wildlife species, highlighting the need for advanced systems for effective conflict management. This study introduces the Geo Wild System (GWS), a novel Progressive Web Application (PWA) specifically tailored for Malaysian contexts. GWS integrates data from diverse sources, including citizen reports, park rangers, and licensed hunters, and employs Geographic Information System (GIS) tools to enhance data visualization and identify conflict hotspots. During the study period from January to June 2024, GWS recorded and facilitated the deployment of 117 traps across six districts: Shah Alam, Hulu Selangor, Sungai Besar, Hulu Langat, Georgetown, and Seberang Prai. These deployments, based on reported conflict data, resulted in the capture of 88 wildlife individuals, including 85 long-tailed macaques (Macaca fascicularis), two wild boars (Sus scrofa), and one Asian palm civets (Paradoxurus hermaphroditus), demonstrating the system's operational effectiveness. GWS supports a comprehensive reporting, monitoring, and analysis system, utilizing GIS-based Kernel Density Estimation (KDE) to pinpoint critical conflict hotspots, particularly in rapidly urbanizing areas. User feedback was overwhelmingly positive, highlighting the system’s user-friendliness and robust data integration. Despite initial deployment challenges and geodetic constraints, GWS has proven to be a valuable tool for mitigating wildlife conflicts, enabling informed, timely management responses, and offering a promising approach to adaptive wildlife management in Malaysia.