Bayesian model averaging by combining deep learning models to improve lake water level prediction

Abstract

Water level (WL) is an essential indicator of lakes and sensitive to climate change. Fluctuations of lake WL may significantly affect water supply security and ecosystem stability. Accurate prediction of lake WL is, therefore, crucial for water resource management and eco-environmental protection. In this study, three deep learning (DL) models, including long short-term memory (LSTM), the gated recurrent unit (GRU), and the temporal convolutional network (TCN), were used to predict WLs at five stations of Poyang Lake for different forecast periods (1-day ahead, 3-day ahead, and 7-day ahead). The forecast results of the three DL models were synthesized through Bayesian model averaging (BMA) to improve prediction accuracy, and Monte Carlo sampling method was used to calculated the 90 % confidence intervals to analyze the model uncertainty. All the three DL models achieved satisfactory prediction accuracy. GRU performed best in most forecast scenarios, followed by TCN and LSTM. None of the models, however, consistently provided the optimal results in all forecast scenarios. Lake WL prediction accuracy of BMA had a further improvement in metrics of NSE and R2 in 80 % of the forecast scenarios and ranked at least top two in all forecast scenarios. The uncertainty analysis showed that the containing ration (CR) values were above 84 % while the relative bandwidth (RB) maintained reliable performance over the 7-day ahead prediction. The proposed framework in the present study can realize satisfactory WL forecast accuracy while avoiding complex comparison and selection of DL models, and it can also be easily applied to the prediction of other hydrological variables.