Resumen
Accurate water level prediction is one of the important challenges in various fields such as hydrology, natural disasters, and water resources management studies. In this study, a deep neural network and a long short-term memory model were applied for water level predictions between 2000 and 2020 in the Phan Rang River Basin of Nihn Thuan located in Vietnam. In addition, a complex network model was utilized to improve the predictive ability of both models for water level prediction at the outlet point of the basin. The water level prediction by each model was compared with the observed water level data, and the predictive power for each model was evaluated using three statistical metrics: the correlation coefficient (CC), the Nash?Sutcliffe efficiency coefficient (NSE), and the normalized root-mean-squared error (NRMSE). Using all data from nearby stations, there may be distortions in the prediction due to unnecessary data for model learning. Therefore, the complex network method was applied to find best data sources providing factors contributing to water level behaviors. The results of this study showed that a combination of the long short-term memory model and the complex network provided the best predictive performance (CC: 0.99; NSE: 0.99; and NRMSE: 0.17) and was selected as the optimal model for water level prediction in this study. As the need for disaster management is gradually increasing, it is expected that the deep learning model with the complex network method have sufficient potential to reduce the damage from natural disasters and improve disaster response systems, such as in the outskirts of Vietnam.