Investigation of the Efficiency of Group Method of Data Handeling and Wavelet Transform in Runoff Forecasting (Case Study: Gharehsoo Watershed)

Rainfall-runoff process is one of the most important and complex phenomena in the hydrological cycle. Therefore, in its modeling, different perspectives for the development and improvement of predictive models have been presented. In this study, while introducing a combination of wavelet-group classification of data, its effectiveness for modeling the rainfall-run-off process in the Ghara-eos watershed was studied. At first, the rainfall and runoff time series were decomposed using a wavelet transform to several sub-basins to overcome its non-state. Then, these time subcircuits are considered as inputs of the grouped data collection method for predicting daily runoff. The efficiency of the combined model with DC and root mean square error (RMSE) were evaluated. The results of the validation of the models indicate that the highest amount of explanation coefficient and the lowest root mean of error for the single GMDH model were 0.65 and 0.07, respectively, and for the combined model of grouping the wavelet data The order is 0.91 and 0.05. The reason for the hybrid model's superiority to the single model is that the combination model of the grouping of wavelet data, instead of using the time series of rainfall and runoff data on a general scale, uses several time- Different decompositions are used as inputs in the model. Also, the results showed that the Wavelet-GMDH combination model compared to other composite models such as Waveline Artificial Neural Network (WANN) due to the GMDH model layer function, which includes binary combinations of input variables, and by selecting the number of optimal neurons in Each layer directs motion to the predicted data, has more efficiency and accuracy.