Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier

Mo Wang, Xiaoping Fu, Dongqing Zhang (), Furong Chen, Jin Su (), Shiqi Zhou, Jianjun Li, Yongming Zhong and Soon Keat Tan
Additional contact information
Mo Wang: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Xiaoping Fu: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Dongqing Zhang: Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Furong Chen: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Jin Su: Faculty of Civil Engineering and Built Environment, University Tun Hussein Onn, Parit Raja 86400, Johor, Malaysia
Shiqi Zhou: College of Design and Innovation, Tongji University, Shanghai 200093, China
Jianjun Li: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Yongming Zhong: Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Soon Keat Tan: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore

Sustainability, 2023, vol. 15, issue 7, 1-16

Abstract: Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes.

Keywords: rural-urban fringe; urban flooding; Bayesian; complex network; adaptive planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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