Prediction of rainfall time series using soft computing techniques

Abstract

Past studies indicate that increasing temperatures would accelerate the Earth's water cycle and in turn would increase the evaporation rate. Increased evaporation will result in more frequent and intense storms; hence, most researchers focus on climate change and its effect on Earth, particularly the precipitation. In the last two decades, the Udaipur district, India, faces water scarcity and flooding situations twice. The present study focuses on the prediction of rainfall using the most advanced soft computing techniques (SCT) such as multivariate adaptive regression splines (MARS), classification and regression trees (CART), and gene expression programming (GEP) in India's Udaipur district. The performance of these SCT was evaluated to test the capability to predict the rainfall. Results showed that the MARS model for rainfall prediction showed better performance than the GEP model.

Keywords: CART; GEP; MARS; Prediction; Rainfall.