A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting

Reservoir modeling and inflow forecasting has a vital role in water resource management/controlling. Hydrological systems' complex nature and problems in their application process have prompted researchers to look for more efficient reservoir inflow forecasting methods; hence, the development of artificial intelligence-based techniques in recent years has caused the hybrid modeling to become popular among hydrologists. To this end, effort has been made in the present study to develop a hybrid model that combines a Long-Short Term Memory (LSTM) algorithm-a special recurrent neural network-with a Convolutional Neural Network (CNN) algorithm for the reservoir inflow forecasting. To forecast the flow data, use was made of the support vector machines (SVM), Long Short-Term Memory (LSTM) algorithm, adaptive neuro-fuzzy inference system (ANFIS), Variable Infiltration Capacity (VIC) and autoregressive integrated moving average (ARIMA) model plus the data collected from the flow measurement stations of Doroodzan Dam reservoir in "Kor"-an important river in Fars Province, Iran. The model estimation results were evaluated by the RMSE, MAE, MAPE, MSE and R-2 statistical criteria and showed that the hybrid CNN-LSTM method was the most successful model by achieving R-2 approximate to 0.9278 (the highest).