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

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 (R-2), 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 R-2 > 80%. For a prediction of 1 day ahead, the proposed models gave an R-2 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.

Automated summary

The objective of this study was to develop an advanced method using LSTM, SVM, and RF to predict streamflow in the Mekong Delta in Vietnam, which is crucial for food security. Water level and flow data from 2014 to 2018 were used as input for the prediction model. The results showed that the SVM and RF models improved the performance of the LSTM model, with R-2 > 80%. LSTM was found to be a robust technique for characterizing and predicting time series behaviors in hydrology applications.