Rapid urban flood forecasting based on cellular automata and deep learning
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.According-ly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrody-namic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limita-tion and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular au-tomata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative er-ror of forecasting the maximum inundation depth in flood-prone locations is less than 10％,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
国家科技期刊平台
NSTL
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