Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China

Abstract

Water level prediction in large dammed rivers is an important task for flood control, hydropower generation, and ecological protection. The variations of water levels in large rivers are traditionally simulated based on hydrological models. Recently, most studies have begun applying deep learning (DL) models as an alternative method for forecasting the dynamics of water levels. However, it is still challenging to directly apply DL to the simultaneous prediction of water levels across multiple sites. This study attempts to develop a hybrid framework by combining the Physical-based Hydrological model (PHM) and Long Short-Term Memory (LSTM). This study hypothesizes that our hybrid model can enhance the predictive accuracy of water levels in large rivers, because it considers the temporal-spatial information of mainstream-tributaries relationships. The effectiveness of the proposed model (PHM-BP-LSTM) is evaluated using the daily water levels from 2012 to 2018 in the Three Gorges Reservoir (TGR), China. Firstly, we use a hydrological model to produce a large amount of water level data to solve the limited training data set. Then, we use the Back Propagation (BP) neural network to capture the mainstream-tributaries relationship. The future changes in water levels in the different mainstream stations are simultaneously predicted by the LSTM model. We reveal that our hybrid model yields satisfactory accuracy for daily water level simulations at fourteen mainstream stations of the TGR. We further demonstrate the proposed model outperforms the traditional machine learning methods in different prediction scenarios (one-day-ahead, three-day-ahead, seven-day-ahead), with RMSE values ranging from 0.793 m to 1.918 m, MAE values ranging from 0.489 m to 1.321 m, and the average relative errors at each mainstream station are controlled below 4%. Overall, our PHM-BP-LSTM, combining physical process and deep learning, can be viewed as a potentially useful approach for water level prediction in the TGR, and possibly for the rapid forecast of changes in water levels in other large rivers.