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Article

Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model

by 1,2, 1,2,*, 1,2, 1,2 and 1,2
1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
Key Laboratory of River Basin Digital Twinning of Ministry of Water Resources, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Academic Editors: Francesco Gallerano and Giuseppe Pezzinga
Water 2022, 14(15), 2282; https://doi.org/10.3390/w14152282
Received: 6 June 2022 / Revised: 11 July 2022 / Accepted: 19 July 2022 / Published: 22 July 2022
(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)
Numerical simulation models are commonly used to analyze and simulate urban waterlogging risk. However, the computational efficiency of numerical models is too low to meet the requirements of urban emergency management. In this study, a new method was established by combining a long short-term memory neural network model with a numerical model, which can quickly predict the waterlogging depth of a city. First, a numerical model was used to simulate and calculate the ponding depth of each ponding point under different rainfall schemes. Using the simulation results as training samples, the long short-term memory neural network was trained to predict and simulate the waterlogging process. The results showed that the proposed “double model” prediction model appropriately reflected the relationship between the changes in waterlogging depth and the temporal and spatial changes in rainfall, and the accuracy and speed of computation were higher than those of the numerical model alone. The simulation speed of the “double model” was 324,000 times that of the numerical model alone. The proposed “double model” method provides a new idea for the application of artificial intelligence technology in the field of disaster prevention and reduction. View Full-Text
Keywords: artificial intelligence; numerical model; “double model” prediction model; urban waterlogging; fast forecasting method; Shenzhen artificial intelligence; numerical model; “double model” prediction model; urban waterlogging; fast forecasting method; Shenzhen
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MDPI and ACS Style

Liu, Y.; Liu, Y.; Zheng, J.; Chai, F.; Ren, H. Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model. Water 2022, 14, 2282. https://doi.org/10.3390/w14152282

AMA Style

Liu Y, Liu Y, Zheng J, Chai F, Ren H. Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model. Water. 2022; 14(15):2282. https://doi.org/10.3390/w14152282

Chicago/Turabian Style

Liu, Yuanyuan, Yesen Liu, Jingwei Zheng, Fuxin Chai, and Hancheng Ren. 2022. "Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model" Water 14, no. 15: 2282. https://doi.org/10.3390/w14152282

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