Rainfall Prediction Using Multiple Linear Regressions Model

Abstract

This research presents a novel approach for
predicting rainfall using a Multiple Linear
Regression model, integrating various
meteorological variables. We focus on a critical
component of the predictive system where historical
weather data is collected and analyzed. Our
developed model effectively identifies patterns and
relationships among different climatic factors. This
process is tailored for specific geographic locations
and will be implemented using a robust
computational framework. The system processes the
collected data, generates forecasts, and automatically
disseminates reports to relevant stakeholders,
ensuring timely agricultural and disaster
preparedness. Although the project is
straightforward, it is distinguished by its application
of advanced statistical techniques for accurate
rainfall prediction. The prediction time has been
significantly reduced, leading to enhanced efficiency
in weather forecasting processes.