A Comprehensive Study of Rainfall Prediction in the Dominant Influence State of Rajasthan (India) Using an Ensemble Linear Regression Approach

Accurate rainfall prediction is crucial for countries like India, with an economy heavily reliant on agriculture. Machine learning techniques, particularly regression models, offer a promising approach for precipitation prediction. The analysis reveals a substantial contrast in rainfall prediction efficacy between the East and West Rajasthan regions. Higher dominance in the East Rajasthan region, indicated by a significantly greater mean absolute difference, emphasizes increased variability and challenges to prediction accuracy. In contrast, the West Rajasthan region exhibits superior rainfall prediction efficacy, with a comparatively lower mean absolute difference. This highlights the critical importance of addressing regional variations in prediction accuracy. The study's insights guide the refinement of rainfall prediction models, particularly focusing on the East Rajasthan region. The successfully trained model, demonstrated by computed results for the entire rainfall dataset independently, showcases remarkable predictive accuracy. This underscores the model's effectiveness in minimizing discrepancies, paving the way for future advancements in refining regional rainfall prediction models. This will further contribute to the development of a robust model with the ability to achieve the lowest absolute error and sensitivity performance among similar models for rainfall prediction.