A Novel Sample-Enhancement Framework for Machine Learning-Based Urban Flood Susceptibility Assessment DOI
Huabing Huang, Changpeng Wang,

Zhiwen Tao

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106314 - 106314

Published: Dec. 1, 2024

Language: Английский

Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas DOI
Erfan Mahmoodi, M. Azari, Mohammad Taghi Dastorani

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(13), P. 5343 - 5363

Published: June 26, 2024

Language: Английский

Citations

5
Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees DOI Creative Commons
Hüseyın Akay

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(13), P. 5365 - 5383

Published: July 11, 2024

Abstract Accurate estimation of flood-damaged zones in a watershed is prominent guiding framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, those analyzed using decision tree algorithms eight novel information fusion techniques to get more reliable susceptibility mapping. The belief function leaf nodes the fused by named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Weighted Averaging (HFWA), Geometric (HFWG), Ordered (HFWOA), HFWOG, Closeness coefficient (C c ) Euclidean Manhattan distances. extracted from generated maps validated receiver operating characteristics (ROC) curve parameters, seed cell area index (SCAI) classified levels. under ROC (AUROC) training process 0.997 DS, HFWA, HFWOA, C -Euclidean, 0.996 -Manhattan, 0.995 FGO 0.994 HFWG HFWOG. AUROC testing 0.951 0.945 FGO, 0.943 HFWG, 0.941 True Skill Statistics 0.962 0.870 processes. Although present excellent performance, SCAI versus classes fitted assess prediction capabilities further. HFWA HFWOG have first- second-best performances on estimations. Hence, paradigm can be employed combine factors based robust classification method predictions potential levels utilize them land use construction planning management.

Language: Английский

Citations

5
Analysis of Flooding Under Extreme Conditions with Factors Interactions Using Hybrid Machine Learning DOI
Yanfen Geng, Xinyu Hu,

Xiao Huang

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Language: Английский

Citations

0
Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate DOI Creative Commons

Justin Diehr,

Ayorinde Ogunyiola, Oluwabunmi Dada

et al.

Annals of GIS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 7, 2025

Language: Английский

Citations

0
FloodCNN-BiLSTM: Predicting flood events in urban environments DOI

Vinay Dubey,

Rahul Katarya

Engineering Analysis with Boundary Elements, Journal Year: 2025, Volume and Issue: 177, P. 106277 - 106277

Published: April 28, 2025

Language: Английский

Citations

0
Assessing Critical Flood-Prone Districts and Optimal Shelter Zones in the Brahmaputra Valley: Strategies for Effective Flood Risk Management DOI
Jatan Debnath, Dhrubajyoti Sahariah, Gowhar Meraj

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: unknown, P. 103772 - 103772

Published: Oct. 1, 2024

Language: Английский

Citations

2
SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks DOI Creative Commons

Krishnagopal Halder,

Anitabha Ghosh,

Amit Kumar Srivastava

et al.

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

Published: Oct. 16, 2024

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in Indian subcontinent, exacerbating threats to human populations economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, Stacking Ensemble—developed Python environment leveraged 18 flood-influencing factors delineate flood-prone areas with precision. A comprehensive inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using Google Earth Engine (GEE) platform, provided empirical for entire model training validation. Model performance was assessed precision, recall, F1-score, accuracy, ROC-AUC metrics. results highlighted Ensemble's superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), SVM (0.920) respectively, establishing feasibility applications disaster management. maps depicting susceptibility flooding generated by current provide actionable insights decision-makers, city planners, authorities responsible management, guiding infrastructural community resilience enhancements against risks.

Language: Английский

Citations

1
Research on Automatic Scoring Algorithm for English Composition Based on Heterogeneous Fusion Network DOI

Jinpeng Zhou,

Zhiyong Tao

Published: June 21, 2024

Language: Английский

Citations

0
A Novel Sample-Enhancement Framework for Machine Learning-Based Urban Flood Susceptibility Assessment DOI
Huabing Huang, Changpeng Wang,

Zhiwen Tao

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106314 - 106314

Published: Dec. 1, 2024

Language: Английский

Citations

0