Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction

Abstract

Many studies have applied the Long Short-Term Memory (LSTM), one of the Recurrent Neural Networks (RNNs), to rainfall-runoff modeling. These data-driven modeling approaches learn the patterns observed from input and output data. It is widely known that the LSTM networks are sensitive to the length and quality of observations used for learning. However, the discussion on a better composition of input data for rainfall-runoff modeling has not yet been sufficiently conducted. This study focuses on whether the composition of input data could help improve the performance of LSTM networks. Therefore, we first examined changes in streamflow prediction performance by various compositions of meteorological variables which are used for LSTM learning. Second, we evaluated whether learning by integrating data from all available basins can improve the streamflow prediction performance of a specific basin. As a result, using all available meteorological data strengthened the model performance. The LSTM generalized by the multi-basin integrated learning showed similar performance to the LSTMs separately learned for each basin but had more minor errors in predicting low flow. Furthermore, we confirmed that it is necessary to group by selecting basins with similar characteristics to increase the usefulness of the integrally learned LSTM.