A Novel method for Rainfall Prediction using Machine Learning

Abstract

Surges are viewed as catastrophic events that can cause setbacks and destroying of infra structures. Uncertainty of rainfall also creates problem, a reduced amount of rainfall and high amount of rainfall both are not desirable henceforth for both the cases water resource management is necessary. Prediction of rainfall can play impotent role for WRM (Water resource management). After studying different literature, work can be carried out using data mining techniques and machine learning model. In this we have proposed a rainfall prediction model which is an integration of clustering data mining technique and multiple regression, which will make efficient and accurate prediction. Proposed algorithm used k- nearest neighbor regression, and we have also implemented k-medoid regression. Further we have passed predicted data to classifier which will generate confusion matrix with two values TPR (True Positive Rate) and FNR (False negative Rate).