Rainfall Patterns in Dakshina Kannada from Predicative analysis using Machine Learning methods

Rainfall prediction is a difficult and uncertain process with huge societal ramifications since it can limit losses in lives and property through early and precise forecasts. This is especially important for agricultural and disaster management in India, where accurate monsoon forecasting is crucial. This study uses machine learning categorization approaches to improve the accuracy of long-term rainfall forecasts. Its specific goal is to create a system that can provide taluks or subdivisions with reliable rainfall estimates for the next few months. In order to do this, the study analyzes statistical data and produces forecasts using linear regression (LR), support vector regression (SVR), and artificial neural networks (ANNs). The findings show that for long-term rainfall prediction, the artificial neural network model performs more accurately and reliably than both linear regression and support vector regression. This development highlights how machine learning methods can enhance rainfall predictions in the face of climate change.