首页|A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

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Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8％that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predict-ing changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation dis-tribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34％in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Convolutional neural networksPhysical continuityRapid predictionUrban pluvial flooding processesWeighted cellular automata

Jiarui Yang、Kai Liu、Ming Wang、Gang Zhao、Wei Wu、Qingrui Yue

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School of National Safety and Emergency Management,Beijing Normal University,Beijing 100875,China

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology,Nanjing 210044,China

Department of Transdisciplinary Science and Engineering,Tokyo Institute of Technology,Tokyo 226-8501,Japan

National Disaster Reduction Center of China,Ministry of Emergency Management,Beijing 100124,China

Research Institute of Urbanization and Urban Safety,University of Science and Technology Beijing,Beijing 100083,China

National Science and Technology Institute of Urban Safety Development,Shenzhen 518046,China

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2024

10.1007/s13753-024-00592-4

国际灾害风险科学学报(英文版)

国际灾害风险科学学报(英文版)

ISSN：
年,卷(期)：2024.15(5)