Advanced Machine Learning Framework for Precision Rainfall Prediction for Jharkhand, India

Jharkhand, characterized by a substantial agricultural population predominantly reliant on rain-fed agriculture, faces significant challenges due to the erratic nature of precipitation. The study uses meteorological variables and historical rainfall data from the Jharkhand Space Agency Centre (JSAC) to predict rainfall with precision and resilience. Three supervised machine learning algorithms, Random Forest, K-Nearest Neighbour (KNN), and Ridge Regression, are employed to evaluate their performance across monsoon and non-monsoon periods. A novel algorithm is proposed for Jharkhand, offering better modularity and accuracy to predict the Rain Index. The results show the efficacy of these algorithms in capturing the temporal variability of rainfall in Jharkhand. The ensemble modeling model obtained an MSE score of 0.457, providing valuable insights into the viability and competence of machine learning algorithms for rainfall estimation. This research offers a valuable scope for policymakers, researchers, and stakeholders to formulate sustainable strategies to address climate variability and its impact on rain-fed agriculture. The study contributes significantly to meteorological research and offers valuable insights for policymakers, researchers, and stakeholders.