Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation

Abstract

With the rapid development of Artificial Intelligence (AI) technology, the Long Short-Term Memory (LSTM) network has been widely used for forecasting hydrological process. To evaluate the effect of training data amount on the performance of LSTM, the study proposed an experiment scheme. First, K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series of 130 years based on the observed data, and the Soil and Water Assessment Tool (SWAT) model is used to obtain the corresponding runoff series with the generated meteorological data series. Then, the 130 years of rainfall and runoff data is divided into two parts: the first 80 years of data for model training and the remaining 50 years of data for model verification. Finally, the LSTM models are developed and evaluated, with the first 5-year, 10-year, 20-year, 40-year and 80-year data series as training data respectively. The results obtained in Yalong River, Minjiang River and Jialing River show that increasing the training data amount can effectively reduce the over-fittings of LSTM network and improve the prediction accuracy and stability of LSTM network.