Combining soft Computing Model Based on Machine Learning Algorithm and Principal Component Analysis for Precipitation Forecasting

The effect of precipitation changes on water resources, agricultural production reveals the need for accurate methods for precipitation forecasting. In this research, one of the soft computing methods was developed in order to forecast precipitation with the data reduction approach. Input data of model was mean air temperature, dew point temperature, mean sea level pressure, mean station pressure, mean relative humidity and mean wind speed at Tabriz, Ahar and Jolfa stations. The method used in this study includes Epsilon and Nu support vector regression. In all studied stations, the use of Nu support vector regression compared to Epsilon reduced the error so that UII values ​​with Nu support vector regression in Tabriz, Ahar and Jolfa stations were decreased 19.19, 5.88 and 15.78%, respectively. The results indicate the limitation of using the data reduction approach for data with KMO factor lower than 0.5, which included Tabriz and Ahar stations. Principal component analysis in both types of support vector regression increased the performance of the model so that in Jolfa station by using principal component analysis d values ​​of Epsilon and Nu support vector regression increased by 16.6 and 17.5%. Execution of Verimax rotation in preprocessing of input data to regression was stronger than principal component analysis. In this regard, RRMSE and RMSE values ​​in Jolfa station using Epsilon support vector regression were decreased 6.66 and 6.45%. Therefore, principal component analysis is a suitable tool to improve the performance of soft computing methods by regarding the relevant constraints.