Daily streamflow prediction based on the long short-term memory algorithm: a case study in the Vietnamese Mekong Delta

Abstract

The objective of this study is the development of a state-of-the-art method based on long short-term memory (LSTM), support vector machine (SVM), and random forest (RF) to predict the streamflow in the Mekong Delta in Vietnam, an area crucial to Vietnam's food security. Water level and flow data from 2014 to 2018 at the Tan Chau station and Can Tho (on the Hau River) were used as the input data of the prediction model. Three different ranges of data – from the preceding 4, 8, and 12 days – were used to predict streamflow for both 1 and 7 days ahead, resulting in six individual predictions. Various statistical indices, namely root-mean-square error, mean absolute error (MAE), and the coefficient of determination (R2), were used to assess the predictive ability of the model. The results showed that the SVM and random forest models were successful in improving the performance of the LSTM model, with R2 > 80%. For a prediction of 1 day ahead, the proposed models gave an R2 value of 2–5% higher than a prediction of 7 days ahead. These results highlighted that LSTM is a robust technique for characterizing and predicting time series behaviors in hydrology applications. HIGHLIGHTS Daily streamflow forecasting was done using hybrid machine learning approaches.; Model performance was evaluated using RMSE, R2, and MAE.; Developed models achieved high accuracy in daily streamflow forecasting.;

Keywords

• long short-term memory
• machine learning
• mekong delta
• streamflow