Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data

Geocarto International, Journal Year: 2025, Volume and Issue: 40(1)

Published: Feb. 10, 2025

Language: Английский

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Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 13, 2025

Language: Английский

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Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 25, 2025

Language: Английский

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Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102285 - 102285

Published: March 4, 2025

Language: Английский

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Cyclone surge inundation susceptibility assessment in Bangladesh coast through geospatial techniques and machine learning algorithms: a comparative study between an island and a mangrove protected area

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: March 18, 2025

Tropical cyclones, including surge inundation, are a joint event in the coastal regions of Bangladesh. The washes out life and property within very short period. Besides, most cases, area remains flooded for several days. Prediction inundation susceptibility due to cyclone is one key issues reducing vulnerability. Surge can be analyzed effectively through geospatial techniques various algorithms. Two techniques, such as GIS-based Analytical Hierarchy Process (AHP) multi-criteria analysis bivariate Frequency Ratio (FR) three algorithms, i.e., Artificial Neural Network (ANN), k -nearest neighbor (KNN) Random Forest (RF), were applied understand comparative level between an island, Sandwip protected by mangrove, Dacope on Bangladesh coast. A total ten criteria considered influential flooding, Elevation, Slope, Topographic Wetness Index, Drainage density, Distance from river sea, Wind flow distance, LULC, NDVI, Precipitation, Soil types. Among them, distance sea (16.34%) elevation (15.01%) found crucial analysis, according AHP expert’s opinions. Similarly, precipitation (9.88) (6.92) LULC (4.16) NDVI (4.33) highest PR values FR analysis. factor maps final ArcGIS 10.8. categorized into five classes, low, moderate, high, high. Very high was around boundary island upper portion upazila. (45.07%) (49.41%) observed KNN ANN, respectively. receiver operating characteristic (ROC) all acceptable prediction; however, possessed better consistent under curve (AUC) value than algorithms both study sites. Policymakers professionals plan manage disaster reduction activities based outcomes.

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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An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand

Progress in Disaster Science, Journal Year: 2024, Volume and Issue: unknown, P. 100393 - 100393

Published: Dec. 1, 2024

Language: Английский

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