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基于LSTM网络的实时入库洪水预报方法
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Abstract:
随着流域水文气象资料观测手段的进步和数据的不断积累，采用数据驱动的方式来研究水库流域入库径流问题，已成为一种可行的思路。本文建立了一种基于LSTM (Long Short-term Memory)网络深度学习算法的入库洪水预报模型，该模型以水库前期入库流量序列、流域前期降雨序列和上游站前期出库流量序列等多个维度作为模型输入，水库预报入库流量作为输出，进行实时洪水预报。安康水库洪水预报的实例验证表明，LSTM网络模型可以很好地模拟入库洪水过程，并在实时洪水预报中也有较好的表现。本研究可为水库防洪及兴利调度提供技术支撑。
With the progress of hydrological and meteorological data observation and the accumulation of data, it has become a feasible way to study the reservoir inflow forecasting by data driven method. This paper established a flood forecasting model based on LSTM (long short term memory) network and depth learning algorithm. The model takes the reservoir early stage inflow, rainfall and upstream sequence as the inputs, and the reservoir inflow as output. The application of Ankang reservoir shows that LSTM network model can simulate the flood hydrograph very well, and has good performance in real-time flood forecasting. This study can provide technique support for reservoir flood control and beneficial operation.
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