Authors: Associate Professor V. Pavani, Challagundla Amrutha, Palanati Sirisha, Ganjapu Sowmya, Gottipatti Tejaswini
Abstract: Precise prediction of rainfall is required in agriculture, management of water resources and mitigation of disasters. The nonlinear and uncertain characteristics of the meteorological data are usually difficult to capture by traditional statistical models. As a solution to this, a hybrid stacking ensemble model based on the combination of Random Forest (RF) and Support Vector Machine (SVM) and Logistic Regression as a meta-classifier is proposed. The model, when using the Rain in Australia data set, has the highest accuracy with a value of over 95% in the present version and the possible accuracy of over 96% with superior prepossessing, feature engineering, and class balancing. The suggested method provides a sure model of enhanced rainfall forecasting, which would be involved in planning the sustainability of agriculture and environmental decision-making.
