Toward explainable flood risk prediction: Integrating a novel hybrid machine learning model
Authors:
- Yongyang WangYWYongyang Wang
- Pan ZhangPZPan Zhang
- Yulei XieYXYulei Xie
- Lei ChenLCLei Chen
- Yu LiYLYu Li
Top Cited Papers
- 11 January 2025
- journal article
- Published by Elsevier in Sustainable Cities and Society
- Vol. 120
Abstract
No abstract availableFunding Information
- Dalian University of Technology
- Ministry of Science and Technology of the People's Republic of China (2022YFC3205100)
- National Natural Science Foundation of China (52439005, 52479005, 52200212)
This publication has 55 references indexed in Scilit:
- Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning AlgorithmEnvironmental Monitoring and Assessment, 2024
- Energy, Sustainability and Resilience2024
- A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and HybridizationsArchives of Computational Methods in Engineering, 2023
- An integrated approach for evaluating dynamics of urban eco-resilience in urban agglomerations of ChinaEcological Indicators, 2023
- Flash flood susceptibility prediction mapping for a road network using hybrid machine learning modelsNatural Hazards, 2021
- A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of ChinaAgricultural Water Management, 2021
- Convolutional neural networks: an overview and application in radiologyInsights into Imaging, 2018
- The Whale Optimization AlgorithmAdvances in Engineering Software, 2016
- Climate-related flood risks and urban responses in the Pearl River Delta, ChinaRegional Environmental Change, 2014
- Transforming Auto-Encoders2011