Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling DOI
Yogesh Bhattarai, Sunil Bista, Rocky Talchabhadel

et al.

Total Environment Advances, Journal Year: 2024, Volume and Issue: 12, P. 200116 - 200116

Published: Oct. 5, 2024

Language: Английский

A multiscale physically-based approach to urban flood risk assessment using ABM and multi-source remote sensing data DOI

Xinyi Shu,

Chenlei Ye,

Zongxue Xu

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105332 - 105332

Published: Feb. 1, 2025

Language: Английский

Citations

1
A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches DOI Creative Commons
Tania Islam, Ethiopia Bisrat Zeleke,

Mahmud Afroz

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 524 - 524

Published: Feb. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Language: Английский

Citations

0
Integrating machine learning with the Minimum Cumulative Resistance Model to assess the impact of urban land use on road waterlogging risk DOI

Xiaotian Qi,

Soon‐Thiam Khu,

Pei Yu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132842 - 132842

Published: Feb. 1, 2025

Language: Английский

Citations

0
Urban Flood Risk Analysis Using the SWAGU-Coupled Model and a Cloud-Enhanced Fuzzy Comprehensive Evaluation Method DOI

Jinhui Hu,

Chunyuan Deng,

Xinyu Chang

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106461 - 106461

Published: April 1, 2025

Language: Английский

Citations

0
Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling DOI
Yogesh Bhattarai, Sunil Bista, Rocky Talchabhadel

et al.

Total Environment Advances, Journal Year: 2024, Volume and Issue: 12, P. 200116 - 200116

Published: Oct. 5, 2024

Language: Английский

Citations

1