Comparative Analysis of ANFIS and SVR Model Performance for Rainfall Prediction

Abstract

In this paper, a comparative study of adaptive neural fuzzy inference system and support vector machine regression models for the purpose of monthly rainfall prediction has been done. The models were trained, validated and tested using 50 years (1960–2010) of historical climatic data of Varanasi, a district in Uttar Pradesh state of India. The data used as the predictors in these models were monthly relative humidity, atmospheric pressure, average temperature and wind speed of Varanasi. The adaptive neural fuzzy inference system (ANFIS) model used in this study is generated using grid partitioning method and its performance was analyzed. The v-support vector regression (v-SVR) model is also developed for the comparative analysis of the rainfall prediction and the kernel used for this model is Radial Basis Function. The performance criteria of these models were Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe model efficient coefficient (E) and correlation coefficient R. In this study, it is observed that the performance of adaptive neural fuzzy inference system (ANFIS) model which is optimized using hybrid method has better performance than v-SVR.