Optimal Kernel and Wavelet Coefficients to Support Vector Regression Model and Wavelet Neural Network for Time Series Rainfall Prediction

Rainfall prediction is an active topic recently since people want to make decisions about crop and irrigation cycles to understand weather and climate patterns. Due to need of predicting this natural phenomenon, various research works has been carried out previously with different type of techniques using historical data. In this paper, a hybrid model based on support vector regression (SVR) model and wavelet neural network (WNN) for rainfall prediction is proposed. In hybridized SVR-WNN, optimal kernel and wavelet coefficients are generated using hybrid algorithm. Here, artificial bee colony (ABC) and genetic algorithm (GA) are hybridized and used to this purpose. These optimal kernel functions and wavelet coefficients are supplied to hybrid model to predict the rainfall. In hybrid model, wavelet neural network with ARX modeling and support vector regression (SVR) model is effectively hybridized to time series rainfall prediction. The performance of the hybrid model is analyzed with the help of real datasets taken from Assam, Chhattisgarh, East Rajasthan, Gangetic West Bengal, Gujarath, Haryana, Telangana, Rajalaseema regions. From the results, it can be concluded that proposed rainfall prediction model have shown the MAPE performance of 20, the RMSE performance of 2, MAD performance of 12, but existing model show the MAPE performance of 61, the RMSE performance of 3, MAD performance of 27 for Telangana dataset.