Rainfall Prediction Using Deep Learning and Machine Learning Techniques

Predicting rainfall is a crucial component of contemporary weather forecasting, with consequences for agriculture, hydrology, water management, and disaster preparedness and response. The conventional method of predicting rainfall has depended on historical information and meteorological observations. Yet, more precise and timely predictions of rainfall patterns are now possible thanks to the development of sophisticated computer models, remote sensing technologies, machine learning, and deep learning algorithms. An overview of the methods for predicting rainfall is given in this work, including the analysis of data from satellite imaging, atmospheric conditions, ocean temperatures, and other climate variables. We also look at how machine learning and deep learning algorithms are evolving to build more sophisticated models that can evaluate vast quantities of data and predict rainfall patterns with greater accuracy.