Monthly Rainfall Analysis in Southern Parts of India Using Machine Learning and Ensemble Methods

Rainfall analysis has a vital function for raising awareness of the dangers of excessive rainfall and for boosting the travel and tourism sector, agriculture, and food security in southern India. Because rainfall is influenced by many different causes, it is challenging to forecast. In this research work, four different machine learning techniques are used: Naïve Bayes, KNN, SVM, and LR, and the accuracy for each model is determined. To improve the accuracy of each model, three different ensemble learning techniques are employed: Stacking, Bagging, and Voting Classifier, to analyze the rainfall data in southern parts of India. Given that Bagging attains the maximum accuracy of 99.5%, it is proposed that Bagging is the best model. The study shows that the ensemble model can enhance prediction quality and boost forecasting accuracy in meteorological applications by replacing traditional forecasting instruments.