Quantifying the Impact of Future Climate Change on Flood Susceptibility: An Integration of CMIP6 Models, Machine Learning, and Remote Sensing-Reference-Cited by-同舟云学术

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Quantifying the Impact of Future Climate Change on Flood Susceptibility: An Integration of CMIP6 Models, Machine Learning, and Remote Sensing

Published:2024-09 Issue:9 Volume:150 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Gholami Farinaz¹^ORCID,Li Yue²,Zhang Junlong³,Nemati Alireza⁴

Affiliation:

1. Research Assistant, School of Automation, Qingdao Univ., Qingdao, Shandong 266071, China (corresponding author). ORCID: .

2. Professor, College of Environmental Science and Engineering, Qingdao Univ., Qingdao, Shandong 266071, China.

3. Assistant Professor, College of Environmental Science and Engineering, Qingdao Univ., Qingdao, Shandong 266071, China.

4. Research Assistant, Dept. of Mechanical and Aerospace Engineering, Univ. of California Davis, Davis, CA 95616.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JWRMD5.WRENG-6344

Reference68 articles.

1. Risk assessment and management of vulnerable areas to flash flood hazards in arid regions using remote sensing and GIS-based knowledge-driven techniques

2. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees

3. Nested hydrological modeling for flood prediction using CMIP6 inputs around Lake Tana, Ethiopia

4. Flood Hazard, Vulnerability and Risk Assessment for Different Land Use Classes Using a Flow Model

5. Integration of hard and soft supervised machine learning for flood susceptibility mapping

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