Rainfall Prediction System using Machine Learning and Deep Learning

Forecasting the amount of rain that will fall each day increases agricultural productivity and guarantees a steady supply of food and water to keep populations healthy.Numerous experiments have been conducted in different countries to forecast rainfall using machine learning and data mining techniques.The agriculture, which is the foundation of the nation's economy, is impacted by the country's uneven rainfall distribution.The nation must plan for and carry out the prudent use of rainfall water in order to mitigate the problems of flooding and drought.This study's primary goal is to determine the pertinent atmospheric factors that contribute to precipitation and utilize artificial intelligence to forecast the daily amount of rain that will fall.The machine learning model's input variables were chosen using the Pearson correlation technique to be pertinent environmental factors.The root mean squared error and mean absolute error methodologies are used to assess the performance of the machine learning model.The study's findings show that the Extreme Gradient Boosting machine learning technique performs better than its rivals..We accomplish this using the machine learning methods of Extreme Gradient Boost, Random Forest, and Multivariate Linear Regression.