Classification-Enhanced LSTM Model for Predicting River Water Levels

River level predicting underpins the management of water resource projects, steers navigational activities in rivers, and protects the lives and properties of riverside communities, etc. Traditionally, hydrological-hydraulic coupled models have been at the forefront of simulating and predicting river levels, achieving notable success. Despite their utility, these models encounter limitations due to the exhaustive demand for various data types—often difficult to obtain—and the ambiguity in determining downstream boundary conditions for the hydraulic model. Responding to these limitations, this study utilizes Long Short-Term Memory (LSTM) model, a deep learning technique, to predict river levels using upstream discharges. Three approaches were used to further enhance the accuracy and reliability of our model. Firstly, we incorporated historical water level data at or downsteam of the predicted station as input, secondly, we classified the datasets based on physical principles, and thirdly, we employed data augmentation techniques. These methods were evaluated within the Jingjiang-Dongting river-lake system in China. It achieves high prediction accuracy of water level and can mitigate the impact of input inaccuracies. The incorporation of water level data as input and the Classification-Enhanced LSTM model that segregates the input data according to rising and recession trends of water level,significantly improve prediction accuracy under extreme water level conditions compared with other deep learning approaches. The proposed model uses easily accessible data to predict water levels, offering enhanced robustness and new strategies for improving prediction accuracy under extreme conditions. It is applicable for predicting water levels at any hydrological station along a river and can enhance the prediction accuracy of hydraulic models by proving a robust downstream boundary condition.