Water level prediction using deep learning models: A case study of the Kien Giang River, Quang Binh Province

Abstract

AbstractTime‐series water level prediction during natural disasters, for example, typhoons and storms, is crucial for both flood control and prevention. Utilizing data‐driven models that harness deep learning (DL) techniques has emerged as an attractive and effective approach to water level prediction. This paper proposed an innovative data‐driven methodology using DL network architectures of Gated Recurrent Unit (GRU), Long Short‐Term Memory (LSTM), and Bidirectional Long‐Short Term Memory (Bi‐LSTM) to predict the water level at the Le Thuy station in the Kien Giang River. These models were implemented and validated based on hourly rainfall and water level observations at meteo‐hydrological stations. Three combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics, that is, R2, MAE, RMSE, Max Error Value, and Max Error Time. The results revealed that the LSTM model outperformed the Bi‐LSTM and GRU models, when water level and rainfall observations for one‐time lag at three stations were used to predict the water level at the Le Thuy station with 1‐h time lead, with the five metrics registering at 0.999; 3.6 cm; 2.6 cm; 12.9 cm; and −1 h, respectively.