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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Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 28, 2024

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human natural ecosystems in Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing machine learning techniques is proposed to address this issue enhancing flood susceptibility understanding informed decision-making. This study utilizes geo-datasets algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, Long Short-Term Memory) generate comprehensive maps. The results highlight Random Forest's superior performance, achieving highest train test Area Under Curve Receiver Operating Characteristic (AUROC) (1.00 0.993), accuracy (0.957), F1-score (0.962), kappa value (0.914), with lowest mean squared error (0.207) Root Mean Squared Error (0.043). Vulnerability particularly pronounced low-elevation low-slope southern downstream areas (Central part PDR). reveal that 36%–53% basin's total area highly susceptible flooding, emphasizing dire need for coordinated floodplain management strategies. research uses freely accessible data, addresses data scarcity studies, provides valuable insights disaster risk sustainable planning

Language: Английский

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Farmers' Flood Adaptation Strategies in the Mohana–Khutiya and East Rapti River Basins in the Chure–Terai Region of Nepal

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: 117, P. 105182 - 105182

Published: Jan. 5, 2025

Language: Английский

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Bagyong Kristine (TS Trami) in Bicol, Philippines: Flood Risk Forecasting, Disaster Risk Preparedness Predictions and Lived Experiences through Machine Learning (ML), Econometrics, and Hermeneutic Analysis

Natural Hazards Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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A Review of Current trends, Challenges, and Future Perspectives in Machine Learning Applications to Water Resources in Nepal

Journal of Hazardous Materials Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100678 - 100678

Published: March 1, 2025

Language: Английский

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Machine Learning-Based Geospatial Flood Prediction: The Case of Brahmaputra Basin

Published: Jan. 1, 2025

Language: Английский

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Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin

VIETNAM JOURNAL OF EARTH SCIENCES, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

The Mekong Basin is the most critical transboundary river basin in Asia. This provides an abundant source of fresh water essential for development agriculture, domestic consumption, and industry, as well production hydroelectricity, it also contributes to ensuring food security worldwide. region often subject floods that cause significant damage human life, society, economy. However, flood risk management challenges this are increasingly substantial due conflicting objectives between several countries data sharing. study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam Stochastic Gradient Descent (SGD), open-source datasets identify probably occurring basin, covering Vietnam Cambodia. Various statistical indices, Area Under Curve (AUC), root mean square error (RMSE), absolute (MAE), coefficient determination (R²), were used evaluate susceptibility models. results show proposed models performed AUC values above 0.8, specifying DNN-Adam model achieved 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD 0.87), XGB 0.82. Regions very high concentrated Delta along River findings supporting decision-makers or planners proposing appropriate mitigation strategies, planning policies, particularly watershed.

Language: Английский

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Transfer learning-based deep learning models for flood and erosion detection in coastal area of Algeria

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 21, 2025

Language: Английский

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History, causes, and trend of floods in the U.S.: a review

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 19, 2024

Language: Английский

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Investigation of the flood event under global climate change with different analysis methods for both historical and future periods

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(8), P. 3939 - 3965

Published: July 8, 2024

ABSTRACT Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in systems as activities such greenhouse-gas emissions increase atmospheric gas concentrations. This situation affects frequency intensity events worldwide, with floods being one them. Floods manifest water inundations due factors rainfall patterns, rising temperatures, erosion, sea-level rise. cause significant damage sensitive areas residential areas, agricultural lands, river valleys, coastal regions, adversely impacting people's lives, economies, environments. Therefore, flood risk has been investigated decision-making processes Diyarbakır province using analytical hierarchy process (AHP) method future disaggregation global model data. HadGEM-ES, GFDL-ESM2M, MPI-ESM-MR models RCP4.5 RCP8.5 scenarios were used. Model data disaggregated equidistance quantile matching method. The study reveals flood-risk findings HadGEM-ES while no was found GFDL-ESM2M models. In AHP method, analysis conducted based on historical across interpreted alongside

Language: Английский

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