Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model

Jian Chen, Yaowei Li, Changhui Zhang (), Yangyang Tian and Zhikai Guo
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Jian Chen: Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yaowei Li: Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Changhui Zhang: Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yangyang Tian: Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Zhikai Guo: Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

IJERPH, 2023, vol. 20, issue 2, 1-12

Abstract: At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow and it is difficult to meet the needs of daily flood control and emergency. How to use artificial intelligence technology to quickly predict urban flooding is a key concern and a problem that needs to be solved. Therefore, this paper combines a numerical model with good computational accuracy and an LSTM artificial neural network model with high computational efficiency to propose a new method for fast prediction of urban flooding risk. The method uses the simulation results of the numerical model of urban flooding as the data driver to construct the LSTM neural network prediction model of each waterlogging point. The results show that the method has a high prediction accuracy and fast calculation speed, which can meet the needs of daily flood control and emergency response, and provides a new idea for the application of artificial intelligence technology in the direction of flood prevention and mitigation.

Keywords: urban flooding; LSTM neural network; numerical model of flooding; quickly predict (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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