Published Paper Details:

The monthly rainfall projections are compared to actual data after training and testing to confirm the model's accuracy. When particular parameters are employed, the model can accurately anticipate monthly rainfall data, according to the findings of this study. Rainfall prediction is one of the most studied areas since it affects the lives and property of many people. Previous rainfall prediction models used a complicated combination of mathematical instruments, but these were insufficient to reach a higher categorization rate. In this study, we propose novel ways for calculating monthly rainfall using Machine Learning Algorithms. Rainfall forecasts are based on quantitative information about the current state of the atmosphere. Complex input-to-output mappings can be learned using a variety of machine learning approaches. The dynamic structure of the atmosphere makes precise rainfall prediction difficult. To forecast future rainfall conditions, the variation in previous year's circumstances must be used. We supported the use of machine Learning Algorithms based on a variety of factors such as temperature, humidity, and wind. Because the suggested algorithm forecasts rainfall based on prior data for a specific geographic area, this prediction will be extremely accurate. When compared to standard rainfall prediction techniques, the model's performance is more accurate.