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.

中文翻译：

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基于MSBP模型的多步序列洪水预报

建立河流流量预测模型可以有效减少洪水造成的巨大损失。本文提出了一种基于多输入多输出策略的多步时间序列预测模型，并将该模型应用于山西某流域洪水预报过程中，有效提高了基于多输入多输出策略的洪水预报模型的工程应用价值。关于深度学习。实验结果表明，在考虑河道季节性特征并筛选影响因素后，简单的神经网络模型可以准确预测峰值、峰值时间和洪水趋势。在此基础上，我们提出了MSBP（Multi-step Back Propagation）模型，可以提前20 h准确预测流域的流量趋势，NSE（Nash Efficiency）为0.89。