基于LSTM模型模拟安康水库洪水过程

Simulation of Ankang Reservoir Inflow Based on LSTM Model

摘要: 安康流域受人类活动等因素影响，下垫面条件发生较大变化，洪水预报系统的误差较大。考虑到水文数据是一种复杂的时间序列，普通的水文模型难以捕捉其变化规律，LSTM (长短期记忆)作为一种有记忆能力的学习网络模型，通过不断输入的新数据，学习时间序列的主要特征和变化趋势，能很好地学习水文数据这种复杂的多变的时间序列。本文利用LSTM网络模型对安康水库洪水过程进行模拟，并与新安江模型进行比较分析，探讨LSTM网络模型在水库洪水预报的适应性。

Abstract: The underlying surface conditions have changed greatly in the Ankang river basin due to human activities, and the error of the flood forecasting system is relatively large. Considering that hydrological data is a complex time series, it is difficult for ordinary hydrological models to capture its changing laws. LSTM (Long-short term memory), as a learning network with memory ability, can learn the complex and chan-geable time of hydrological data well by continuously inputting new data and learning the main features and changes of time series sequence. The LSTM network model is used to simulate the flood process of Ankang reservoir and compare with the Xinanjiang model. The adaptability of LSTM model in reservoir flood prediction is also discussed.