A Rainy Outlook: Machine Learning Approaches for Predicting Precipitation

Rishab Chopra

I.  ABSTRACT

Predicting rainfall is one of the most significant topics in meteorological and hydrological research. Conventional methods use statistical models and historical weather data to predict patterns of precipitation. Conversely, machine learning has made it possible to estimate rainfall more precisely. This publication presents a study on the application of machine learning techniques for rainfall prediction. Training models on a wide range of meteorological factors, such as temperature, humidity, wind speed, and air pressure, may significantly improve the accuracy of rainfall forecasts. The results of the experiment demonstrate that machine learning can accurately predict rainfall more than traditional methods, making it a valuable tool in this regard. This research highlights the value of machine learning as a predictor of rainfall patterns, with potential uses in disaster preparation, agriculture, and water resource management.