摘要
Abstract
Taking the Hanyu River Basin in Guangdong Province as a typical research object,this paper aims to improve the traditional hydrological model through advanced data-driven method,so as to improve the accuracy of flood process simulation.This paper proposes a new hybrid model of LSTM-SWMM,which combines the advantages of SWMM physical model and LSTM neural network.The results show that the flood process simulated by LSTM-SWMM model is in good agreement with the actual observed flow.The R2 value of the model is 0.97,and the MAE and RMSE are 81.3 m3/s and 148.6 m3/s respectively.The R2 value of LSTM control model was 0.91,and the MAE and RMSE were 100.8 m3/s and 192.4 m3/s,respectively.Because of its unique design,LSTM-SWMM network shows significant advantages in the process of flood prediction.关键词
LSTM网络/SWMM模型/洪水过程/模拟Key words
LSTM network/SWMM model/Flood process/simulate分类
建筑与水利
