Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accurately predict the peak value, the peak time and flood trends. On this basis, we proposed the MSBP (Multi-step Back Propagation) model, which can accurately predict the flow trend of the river basin 20 h in advance, and the NSE (Nash Efficiency) is 0.89. The MSBP model can improve the reliability of flood forecasting and increase the internal interpretability of the model, which is of great significance for effectively improving the effect of flood forecasting.
A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach
