Reservoir inflow prediction using a hybrid model based on deep learning

Abstract

Reservoir inflow prediction plays a significant role in the field of hydrological prediction. Accurate and reliable prediction of reservoir inflow is the key to flood control decision. In this paper, we combine the deep belief network (DBN) with the Long Short-Term Memory (LSTM) to present a hybrid model based on deep learning (HDL) for reservoir inflow prediction. We take a full consideration of the basin flow and rainfall factors, which significantly affect the inflow flow. According to the rainfall data, we divide the corresponding flow data into two cases: rain and no rain. The proposed approach consists of three parts: we apply the DBN to learn the characteristics of the flow data and get predicted values of the reservoir inflow in case of rain and in case of no rain respectively. Then, we use the basin rainfall data and adopt the LSTM to fit the differential between the predicted inflow value generated by DBN in case of rain and the real inflow value, and get the predicted differential. Finally, the outputs of these three parts are added to obtain the final predicted result. Experiments are evaluated by the historical flow and rainfall data of a reservoir in China, and the results have proved that our method is effective and has higher prediction accuracy.