Development of Ensemble Probabilistic Machine Learning Models for Rainfall Predictions

Rain is of paramount importance for Indian agriculture, as it serves as the primary source of water for crops, sustaining agricultural productivity and ensuring food security for millions of people. In India’s predominantly rain-fed agriculture, timely and adequate rainfall is crucial for successful crop growth, making it a lifeline for farmers and a determining factor in the country’s agricultural output. Accurate rainfall predictions are essential for various applications, including agriculture, water resource management, and disaster preparedness. Ensemble machine learning models have demonstrated their capability to enhance the accuracy and reliability of rainfall predictions compared to single models. This article presents a comparative analysis of different ensemble techniques applied to rainfall prediction tasks. We explore various ensemble approaches, including Averaging, Max Voting, and Stacking, and evaluate their performance using rainfall day-wise datasets from Pantnagar (29.0222° N, 79.4908° E), Uttarakhand, India, from 2010 to 2022.