Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

AbstractThis study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those of the rainfall-runoff model, and different training...

Full description

Bibliographic Details
Main Authors: Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, Karim I. Abdrabo, Sameh A. Kantoush, Tetsuya Sumi, Hamouda Boutaghane, Tomoharu Hori, Doan Van Binh, Binh Quang Nguyen, Thao T. P. Bui, Ngoc Duong Vo, Emad Habib, Emad Mabrouk
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Machine learning
random forest
LightGBM
CatBoost
flood susceptibility mapping
rainfall-runoff inundation model
Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2023.2203798

Internet

https://www.tandfonline.com/doi/10.1080/19475705.2023.2203798

Similar Items