Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

中文翻译：

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使用长短期记忆网络进行河流流量预测

近年来，深度学习在方法论和实际应用方面取得了重大进展。然而，人们对长短期记忆（LSTM）网络在河流流量预测中的表现缺乏了解。本文评估了 LSTM 网络的性能，以了解网络结构和参数对河流流量预测的影响。分别以浑河流域和长江上游流域两个特征不同的流域为例进行10天平均流量预测和日流量预测。在 LSTM 单元层之前使用带有激活函数的全连接层可以大大降低学习效率。相反，由于降水和流量的大小不同，需要在 LSTM 单元之后进行非线性变换以提高学习效率。批量大小和 LSTM 单元的数量是敏感参数，应仔细调整以实现学习效率和稳定性之间的平衡。与几种水文模型相比，LSTM网络在决定系数、纳什-萨特克利夫效率和相对误差三个评价标准上都取得了良好的性能，显示了其在水文建模中学习非线性和复杂过程的强大能力。