Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

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