Journal Article

Using long short-term memory networks for river flow prediction

Jiang, Yanan, Zhang, Xiaoli, Xu, Wei, Li, Yi, Zhang, Run, Fu, Guangtao

2020

Overview

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.

• Publisher: IWA Publishing
• Subject: Average flow / Basins / Cells / Deep learning / Efficiency / Hydrologic models / Hydrologic processes / hydrological modelling / Hydrology / Linear transformations / Long short-term memory / lstm / machine learning / Networks / Neural networks / Parameter sensitivity / Parameters / Performance assessment / Precipitation / Predictions / Rain / River basins / River flow / river flow prediction / Rivers / Runoff / Stability / Stream flow / Time series