Development of Multiple Combined Regression Methods for Rainfall Measurement

Rainfall forecast is imperative as overwhelming precipitation can lead to numerous catastrophes. The prediction makes a difference for individuals to require preventive measures. In addition, the expectation ought to be precise. Most of the nations in the world is an agricultural nation and most of the economy of any nation depends upon agriculture. Rain plays an imperative part in agribusiness so the early expectation of rainfall plays a vital part within the economy of any agricultural. Overwhelming precipitation may well be a major disadvantage. It’s a cause for natural disasters like floods and drought that unit of measurement experienced by people over the world each year. Rainfall forecast has been one of the foremost challenging issues around the world in the final year. There are so many techniques that have been invented for predicting rainfall but most of them are classification, clustering techniques. Predicting the quantity of rain prediction is crucial for countries' people. In our paperwork, we have proposed some regression analysis techniques which can be utilized for predicting the quantity of rainfall (The amount of rainfall recorded for the day in mm) based on some historical weather conditions dataset. we have applied 10 supervised regressors (Machine Learning Model) and some preprocessing methodology to the dataset. We have also analyzed the result and compared them using various statistical parameters among these trained models to find the bestperformed model. Using this model for predicting the quantity of rainfall in some different places. Finally, the Random Forest regressor has predicted the best r2 score of 0.869904217, and the mean absolute error is 0.194459262, mean squared error is 0.126358647 and the root mean squared error is 0.355469615.