Integration of deep learning models for mineral prospectivity mapping: a novel Bayesian index approach to reducing uncertainty in exploration

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(3)

Published: March 6, 2025

Language: Английский

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A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Floodplain, Assam

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 467, P. 142985 - 142985

Published: June 28, 2024

Language: Английский

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Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(35), P. 48497 - 48522

Published: July 20, 2024

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due climate change exacerbating extreme weather events robust flood modeling crucial support disaster resilience adaptation. This study uses multi-sourced geospatial datasets develop an advanced machine learning framework for assessment in the Arambag region of West Bengal, India. The inventory was constructed through Sentinel-1 SAR analysis global databases. Fifteen conditioning factors related topography, land cover, soil, rainfall, proximity, demographics were incorporated. Rigorous training testing diverse models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, MARS algorithms, undertaken categorical mapping. Model optimization achieved statistical feature selection techniques. Accuracy metrics model interpretability methods like SHAP Boruta implemented evaluate predictive performance. According area under receiver operating characteristic curve (AUC), prediction accuracy models performed around > 80%. RF achieves AUC 0.847 at resampling factor 5, indicating strong discriminative AdaBoost also consistently exhibits good ability, with values 0.839 10. indicated precipitation elevation as most significantly contributing area. Most pointed out southern portions highly susceptible areas. On average, from 17.2 18.6% hazards. In analysis, various nature-inspired algorithms identified selected input parameters assessment, i.e., elevation, precipitation, distance rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, roads, gMIS. As per analyses, it found that rivers play roles decision-making process assessment. results majority building footprints (15.27%) are high very risk, followed by those low risk (43.80%), (24.30%), moderate (16.63%). Similarly, cropland affected flooding this categorized into five classes: (16.85%), (17.28%), (16.07%), (16.51%), (33.29%). However, interdisciplinary contributes towards hydraulic hydrological management.

Language: Английский

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Understanding Rainfall Distribution Characteristics over the Vietnamese Mekong Delta: A Comparison between Coastal and Inland Localities

Atmosphere, Journal Year: 2024, Volume and Issue: 15(2), P. 217 - 217

Published: Feb. 10, 2024

This study examines the changing rainfall patterns in Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ Mann–Kendall test, sequential and innovative trend analysis investigate trends annual, wet, dry season rainfall, as well daily events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual seasonal compared further inland stations of Can Tho Moc Hoa. Interestingly, Mau experienced notable decrease rainfall. Conversely, Tho, an overall some months wet increase Furthermore, Hoa number rainy days, especially during season. Principal component (PCA) revealed strong correlations between extreme weather events, particularly for emphasizing complex interplay geographic climatic factors within region. findings offer insights policymakers planners, thus aiding development targeted interventions manage water resources prepare climate conditions.

Language: Английский

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Artificial neural networks for flood susceptibility analysis in Gangarampur sub-division of Dakshin Dinajpur, West Bengal, India

Frontiers in Engineering and Built Environment, Journal Year: 2025, Volume and Issue: 5(1), P. 1 - 21

Published: Jan. 31, 2025

Purpose The study aims to identify the areas of flood susceptibility and categorize Gangarampur sub-division into various zones. It also aspires evaluate efficacy integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for analysis. Design/methodology/approach factors contributing floods such as rainfall, geomorphology, geo-hazard, elevation, stream density, land use cover, slope, distance from roads, Normalized Difference Water Index (NDWI) rivers were analyzed ANN model helps construct map area. For validating outcome, Receiver Operating Characteristic (ROC) is employed. Findings results indicated that proximity rivers, rainfall deviation, cover are most significant influencing occurrence in demonstrated a prediction accuracy 85%, its effectiveness Originality/value research offers novel approach by analysis sub-division. By identifying key deviation use, achieves 85% accuracy, showing risk mapping. These findings provide critical insights planners devise targeted mitigation strategies.

Language: Английский

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A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

Water, Journal Year: 2024, Volume and Issue: 16(14), P. 2069 - 2069

Published: July 22, 2024

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed trends such context flash floods. This study reviews innovative as artificial intelligence (AI)/machine learning (ML), Internet Things (IoT), cloud computing, and robotics used flood early warnings susceptibility predictions. Articles published between 2010 2023 were manually collected from scientific databases Google Scholar, Scopus, Web Science. Based on review, AI/ML applied to warning prediction 64% papers, followed by IoT (19%), computing (6%), (2%). Among most common methods predictions are random forests support vector machines. further optimization emerging technologies, computer vision, required improve these technologies. algorithms demonstrated accurate performance, with receiver operating characteristics (ROC) areas under curve (AUC) greater than 0.90. there is a need current models large test datasets. Through AI/ML, IoT, can be disseminated targeted communities real time via electronic media, SMS social media platforms. In spite this, systems issues internet connectivity, well data loss. Additionally, Al/ML number topographical variables (such slope), geological lithology), hydrological stream density) predict susceptibility, but selection lacks clear theoretical basis inconsistencies. To generate more reliable risk assessment maps, future should also consider sociodemographic, health, housing data. Considering climate change impacts, or may projected different scenarios help design long-term adaptation strategies.

Language: Английский

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Analysis of Uneven Settlement of Long-Span Bridge Foundations Based on SBAS-InSAR

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 248 - 248

Published: Jan. 11, 2025

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment labor-intensive costly, especially long-span bridges. Current remote sensing methods have not been thoroughly evaluated capability detect analyze complex patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (SBAS-InSAR) technology monitor of a bridge. Our analysis revealed distinct deformation patterns: uplift the north bank approach bridge left-side main (maximum rate: 36.97 mm/year), concurrent subsidence right-side south 35.59 mm/year). We then investigated relationship between these various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness (TWI), precipitation, temperature. The observed were attributed combined effects stratigraphic heterogeneity, dynamic hydrological seasonal climate variations. These findings demonstrate that SBAS-InSAR effectively capture processes, offering cost-effective alternative methods. This advancement could enable more widespread frequent assessment stability, ultimately improving management.

Language: Английский

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Spatiotemporal dynamics of social vulnerability to natural hazards: Trends and projections from 2002 to 2030 in northwestern Iran

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106172 - 106172

Published: Jan. 1, 2025

Language: Английский

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Geo-environmental GIS modeling to predict flood hazard in heavy rainfall eastern Himalaya region: a precautionary measure towards disaster risk reduction

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(2)

Published: Feb. 1, 2025

Language: Английский

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A Comparison of the AHP and BWM Models for the Flash Flood Susceptibility Assessment: A Case Study of the Ibar River Basin in Montenegro

Water, Journal Year: 2025, Volume and Issue: 17(6), P. 844 - 844

Published: March 14, 2025

Assessing flash flood susceptibility is crucial for disaster management, yet Montenegro lacks research using geoinformation technologies. In northeastern Montenegro, the Ibar River Basin, mainly in Rožaje, has a well-developed hydrological network with torrential streams prone to flooding. This study compares two multi-criteria GIS decision analysis (GIS–MCDA) methodologies, Analytic Hierarchy Process (AHP) and Best-Worst Method (BWM), assessing susceptibility. The uses Flash Flood Susceptibility Index (FFSI), integrating geoenvironmental climatic factors. criteria considered include terrain slope, distance from drainage network, geology, land cover, density, bare soil index, BIO16 variable, which represents mean monthly precipitation of wettest quarter enhance pattern assessment. AHP model classifies 2.78% area as high very susceptibility, while BWM identifies 3.21% these categories. Both models perform excellently based on AUC values, minor, non-significant differences. Sensitivity shows provides more stable weight distribution, whereas sensitive changes, emphasizing dominant strongly. introduces first time modeling, demonstrating its suitability key novelty lies comparative AHP, highlighting differences distribution stability.

Language: Английский

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