Impact of training data size on the LSTM performances for rainfall–runoff modeling

Abstract

For predicting catchment runoff with data-driven methods, a long historical database of measurements is required. The current study focuses on the assessment of a deep learning model named long short-term memory (LSTM) for rainfall–runoff relationship with different training data size. The developed model has been evaluated on twenty catchments with diverse hydrological conditions obtained from the freely available CAMELS dataset. In order to prove the efficiency of the proposed model for runoff prediction, we test its performances against the traditional feed-forward neural network model. The studied models have been trained with the same input parameters and different size of training data to show the effect of data length on the prediction performances. To this end, the length of training data was varied from 3 to 15 years, while the model was tested on 10 years of data. The results show that the deep LSTM outperforms the traditional model in terms of statistical indicators over different size of training sets. The proposed deep LSTM model can predict runoff with acceptable performances using 9 years of data length in the training procedure, a result that improves when using 12 years. In addition, it has been proven that the deep LSTM model may be efficient even when using small data size (3 years) compared to its benchmarked model which require 9 years for similar results. Thus, the LSTM network is a powerful deep learning model able to learn the behavior of rainfall–runoff relationship with a minimum data length.