Prediction River Discharge Using the Combined Method of Long Short-Term Memory, Wavelet Transform and Empirical Mode Decomposition in Semi-Arid and Humid Climate

Iran is faced with a dry and semi-dry climate with destructive floods, droughts, and water shortages. Droughts and floods can affect the environment, economic, and social activities. Therefore, examining and predicting river discharge and planning management to control it, especially for future water consumption, is very valuable. In this study, changes in river discharge were modeled using statistical data from 2001 to 2020.Statistical data from synoptic and hydrometric stations in a semi-arid region in Urmia city of West Azerbaijan province and a humid region in Amol city of Mazandaran province were used. Out of twelve time-series models defined for the Long Short-Term Memory (LSTM) network, the best model was identified. Then, LSTM modeling was performed based on pre-processing methods of Discrete Wavelet Transform (DWT) and Complementary Ensemble Empirical Mode Decomposition (CEEMD). The results showed that the selected model has high ability and efficiency in estimating the amount of river discharge. On the other hand, pre-processing methods improved the results such that the DC evaluation criterion in the wavelet transform increased from 0.93 to 0.95 in the Nazloo River and from 0.83 to 0.90 in the Chalous River. The best evaluation results for test data using wavelet transform for the Nazloo River in the semi-arid climate with evaluation criteria of R=0.977, DC=0.954, and RMSE=0.018 were obtained. Furthermore, the results of the sensitivity analysis indicated that the discharge parameter of one day before is the most effective in daily discharge estimation.