Performance Assessment of Gene Expression Programming Model Using Data Preprocessing Methods to Modeling River Flow

Background and Objectives: An increasing need to water causes the importance of planning management in order to control water consumption in the future. River flow prediction, in addition to the management of water resources, can predict natural disasters such as flood and drought. Therefore, an accurate estimation of river flow using different models is an issue which has been considered by different water resource researchers. Intelligent models have been used to predict river flow. One of these models, which have shown appropriate performance, is Gene Expression Programming (GEP). A use of intelligent models in combinations has been lately accepted and for this purpose, the wavelet transform is usually used.
Materials and Methods: In this study, the GEP model was used for modeling flow in the daily and monthly scale in Gamasiyab River. For this purpose, data of precipitation, temperature, evaporation and flow Gamasiyab River in Varayeneh Station was used during the period from 1970 to 2012. To increase the accuracy of the model, two methods of data pre-process, called Wavelet transform, and principal components analysis (PCA) and were used in such a way that the primary signal of each input parameter was decomposed using the wavelet transform. Then, to determine main sub-signals, the principal components analysis was used and main sub-signals as inputs were entered into the GEP model to produce Wavelet- Gene Expression Programming (WGEP).
Results: Detection of different structures of the GEP model showed that the performance of the model was good on the daily scale, but in the monthly scale, the performance was reduced. The comparison of the WGEP model with The GEP model showed that the performance of the hybrid model in both of the daily and monthly scale was better than the simple model. It’s because of a pre-process which was done on data. The results of the hybrid model, based on the coefficient determination, was increased by 4% on the daily scale, and by 23% in the monthly scale. Also, regarding too many sub-signals, using the Principal Components Analysis increased the speed of running.
Conclusion: Using pre-process of data has increased the performance of the model and using the PCA, as an auxiliary tool for the wavelet transform, increased the speed and accuracy of the model. Totally, the results showed that it’s possible to use the GEP model with the wavelet transform as a suitable tool for modeling and predicting the flow of Gamasiyab River.