River Discharge Forecasting in Mahanadi River Basin Based on Deep Learning Techniques

This paper presents the applicability of deep learning techniques for river discharge forecasting. Deep learning techniques have been an area of interest for forecasting in engineering because of their forecasting accuracies. Two deep learning techniques long-short term memory (LSTM) and bidirectional long-short term memory (Bi-LSTM) have been applied and compared for forecasting river discharge data in this study. In LSTM, input flows in one direction (forward) as it has only one layer. But in Bi-LSTM, input flows in two directions (forward and backward) as it has an additional layer and the output of both layers is combined together for the final output. For the evaluation of this study, daily discharge data of the Salehbhata station in the Mahanadi River basin has been used. Two statistical measures which are Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE) were used for model evaluation. Results show that both models perform well but Bi-LSTM is slightly better than LSTM in terms of both statistical measures. These results of both models are with different values of hyperparameters. The performance of these models can be different with the same values of hyperparameters and comparison of both models with the same hyperparameters may not be ideal. From this study, it is concluded that Bi-LSTM can be an alternative approach for forecasting river discharge data.