Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Abstract

Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes namely feature selection, prediction, and parameter tuning. Initially, the CSMO algorithm is employed to derive a useful subset of features and reduce the computational complexity. In addition, the KRR model is used for the prediction of rainfall based on weather data. Lastly, the symbiotic organism search (SOS) algorithm is employed to properly tune the parameters involved in it. A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different measures. The simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.