Water Level Prediction and Forecasting Using a Long Short-Term Memory Model for Nam Ngum River Basin in Lao PDR

Abstract

The process of implementing neural networks in a computer system is known as deep learning. In this study, a deep learning model, namely long short-term memory (LSTM), was established to predict and forecast water levels for stations located at the Nam Ngum River Basin in Lao PDR. Water levels are predicted and forecasted based on the rainfall and water level data observed at previous time steps. It is proposed that the optimal sequence length for modeling should be determined based on the threshold of the correlation coefficient obtained from the water level and rainfall time series. The trained LSTM models in this study can be considered fair and adequate for water level prediction, as NSE values from 0.5 to 0.7 were mostly obtained from the model validations in the testing periods. The results showed that the autocorrelation and cross-correlation analysis did help in determining the optimal sequence length in an LSTM model. The performance levels of the LSTM model in forecasting future water levels in the Nam Ngum River Basin varied; the forecasted water level hydrographs for the Pakkayoung station generally corresponded with the observed ones, while the forecasted water level hydrographs for the other stations deviated significantly from the observed hydrographs.