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

Geomatics Natural Hazards and Risk, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 13, 2025

Язык: Английский

---

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

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102285 - 102285

Опубликована: Март 4, 2025

Язык: Английский

---

A novel flood conditioning factor based on topography for flood susceptibility modeling

Geoscience Frontiers, Год журнала: 2024, Номер 16(1), С. 101960 - 101960

Опубликована: Ноя. 1, 2024

Язык: Английский

---

Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective

Journal of Environmental Management, Год журнала: 2025, Номер 373, С. 123842 - 123842

Опубликована: Янв. 1, 2025

Язык: Английский

---

Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model

Sensors, Год журнала: 2025, Номер 25(8), С. 2503 - 2503

Опубликована: Апрель 16, 2025

Accurate flood monitoring and forecasting techniques are important continue to be developed for improved disaster preparedness mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting patterns environmental relationships that may overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference estimate areas long short-term memory (LSTM) model combination of soil moisture information, rainfall forecasts, floodplain topography. To perform modeling LSTM, datasets different spatial resolutions were resampled 30 m resolution bicubic interpolation. The model’s efficacy quantified validating the LSTM-based inundation area mask from Senti-nel-1 SAR images regions topographic characteristics. average under curve (AUC) value LSTM 0.93, indicating high accuracy FW. confusion matrix-derived metrics validate had high-performance ~0.9. SMAP FW showed optimal performance low-covered vegetation, seasonal variations flat regions. estimates show methodological promise proposed framework resilience.

Язык: Английский

---

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

VIETNAM JOURNAL OF EARTH SCIENCES, Год журнала: 2025, Номер unknown

Опубликована: Апрель 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.

Язык: Английский

---

A novel approach in comparing the performance of bivariate statistical methods, boosting, and stacking models in flood susceptibility assessment

Journal of Environmental Management, Год журнала: 2025, Номер 387, С. 125670 - 125670

Опубликована: Май 19, 2025

Язык: Английский

---

Novel Kolmogorov-Arnold network architectures for accurate flood susceptibility mapping: a comparative study

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133553 - 133553

Опубликована: Май 1, 2025

Язык: Английский

---

A Novel Sample-Enhancement Framework for Machine Learning-Based Urban Flood Susceptibility Assessment

Environmental Modelling & Software, Год журнала: 2024, Номер unknown, С. 106314 - 106314

Опубликована: Дек. 1, 2024

Язык: Английский

---