An XGBoost-SHAP framework for identifying key drivers of urban flooding and developing targeted mitigation strategies
Guangzhou Urban Planning Survey & Design Institute · Guangdong University of Petrochemical Technology · +4 more institutions
Abstract
• Train a model to identify the driving factors of urban flooding. • Construct a scenario to analysis the spatial heterogeneity of urban flooding. • Propose corresponding adaptive strategies for urban flooding disasters. Urban flooding is a multifaceted and severe issue, exacerbated by global climate change and urban expansion. Hence, it is imperative to investigate effective strategies for mitigating the urban flooding risk. This research proposes an innovative framework to recognize the driving factors and evaluate the impact of land cover changes on urban flooding, using a machine learning simulation model in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The results indicated that approximately…
Citation impact
- FWCI
- 39.65
- Percentile
- 100%
- References
- 59
Authors
7- XFXiaoping Fu
Guangzhou Urban Planning Survey & Design Institute
- MWMo WangCorresponding
Guangzhou Urban Planning Survey & Design Institute
- DZDongqing ZhangCorresponding
Guangdong University of Petrochemical Technology
- FCFu‐Rong Chen
Guangzhou Vocational College of Science and Technology
- XPXiaotao Peng
Ministry of Natural Resources
Topics & keywords
- Flooding (psychology)
- Key (lock)
- Environmental planning
- Environmental resource management
- Environmental science
- Ecology
- Geography
- Computer science
- Climate action