The effect of preprocessing and reducing the input dimensions of the flow prediction model on optimized support vector regression by genetic algorithm

Accurate and correct prediction of surface water flow plays an important role in the principled planning and proper management of water resources. To achieve this, various prediction models using mathematical relationships based on hydrological information can be used. In this study, monthly discharge of Polechehr hydrometric station for a 48-year has been used (Sep. 2018-October 1971). Two main scenarios with and without pre-processing (standardization), two time series or non-time series approaches were considered. Also, two cases with and without feature selection have been considered by a random forest algorithm. In all cases, 80% and 20% of the data are intended for model training and testing, respectively. The entire coding process is done in the Python programming platform. Genetic algorithm was used to optimize the parameters of the support vector regression method. The results showed that standardization, non-time series approach, reducing the dimensionality of the model input to select and also using genetic algorithm to optimize the parameters of the support vector regression model have the greatest effect on prediction accuracy, respectively. So that the highest coefficient of explanation for training data is 0.85 and for testing is equal to 0.6.If standardization is not applying on the data, adopting a time series approach and feature selection will lead to better results in predicting the SVR model, and also the use of genetic algorithm optimizer compared to the simple model will have a significant effect on improving results.