Prediction Rainfall with Regression Analysis

Abstract

Abstract: Weather forecasting is one of the many widely used applications of artificial intelligence. Forecasting precipitation is one of the most popular research topics because it results in a great deal of property damage and numerous fatalities. Large-scale flooding can have an impact on a variety of social and practical spheres, including agriculture and disaster preparedness. Even with the most advanced mathematical techniques, older, widely used precipitation prediction models were unable to achieve higher classification rates. This article introduces a cutting-edge new technique for forecasting monthly precipitation that makes use of linear regression analysis. Using quantitative data about the state of the atmosphere, forecast when it will rain. Complex information can be recognized by some machine learning systems. a mapping that joins inputs and outputs with a small number of samples. Because of how quickly the atmosphere may change, it is challenging to anticipate precipitation with absolute confidence. The variation in conditions from the previous year should be used to forecast the likelihood of precipitation. For several factors like temperature, humidity, and wind, I advise utilizing linear regression. Given that the suggested model frequently estimates precipitation based on historical data for a specific geographic area, this forecast should be more accurate. Comparing the model's performance to wellknown methods for precipitation prediction, it performs more accurately.