Flood Forecasting for Small Reservoirs Based on Neural Networks - PENG Wei; XIONG Jiayi; JIANG Xianqun; GAO Yueming (Renmin Zhujiang)
In flood forecasting,empirical prediction methods report low accuracy,and traditional hydrological models face the problems of large workloads and difficult promotion when they are applied to small reservoirs.Hence,an artificial neural network (ANN) method is introduced,which is equipped with powerful feature-learning capability.It is combined with the genetic algorithm (GA) to find the optimal parameters for flood forecasting of small reservoirs as GA can realize automatic optimization of the time step and hidden-layer neuron nodes in ANN.In this way,parameter search can be targeted,and personalized flood forecasting models can be constructed for each small reservoir.In addition,the flood forecasting models based on the back propagation (BP),long short-term memory (LSTM),and gated recurrent unit (GRU) neural networks are built,and comparisons between simulations and measured data are conducted for the flood process.The results show that the LSTM model has high prediction accuracy and good stability and can learn and simulate the water-level change pattern of the actual flood process,demonstrating better prediction performance than BP and GRU models.