Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models

Jialei Chen; Guoru Huang; Wenjie Chen

doi:10.1016/j.jenvman.2021.112810

Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models

J Environ Manage. 2021 Sep 1:293:112810. doi: 10.1016/j.jenvman.2021.112810. Epub 2021 May 21.

Authors

Jialei Chen¹, Guoru Huang², Wenjie Chen³

Affiliations

¹ School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China.
² School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China; Guangdong Engineering Technology Research Center of Safety and Greenization for Water Conservancy Project, Guangzhou, 510640, China.
³ School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Guangdong Engineering Technology Research Center of Safety and Greenization for Water Conservancy Project, Guangzhou, 510640, China. Electronic address: wjchen@scut.edu.cn.

PMID: 34029980
DOI: 10.1016/j.jenvman.2021.112810

Abstract

Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.

Keywords: Flood risk assessment; Gradient boosting decision tree; Machine learning; Risk management; The Pearl River Delta.

MeSH terms

Disasters*
Floods*
Machine Learning
Neural Networks, Computer
Risk Assessment