A Machine Learning Approach to Statistical Analysis and Prediction of Rainfall and Drought in the Marathwada Subregion

Abstract

Monitoring, mitigating, and forecasting rainfall has been a concern on a global basis up to now. Numerous natural disasters, such as drought, are directly related to it and are impacted by it. Drought is the most hazardous of all the disasters. Identifying drought is difficult as it has no universal definition. It varies from region to region and climate to climate. There are various contributing factors in the judgment. It can be regional resources like climate, soil type, flora and fauna, precipitation, crop culture, etc. Also, many indicators are available that can define a drought and its type. Scientists have tried to find the most reliable indicator to identify the drought. They have concluded that no best indicator exists. In order to find the best fit, researchers recommend focusing on regional resources. The goal of the study is to make an analysis of the rainfall in the semi-arid region of Marathwada and implement a suitable machine learning approach to enhance the outcome. Over 41 years of regional precipitation data are used for the analysis. The monthly rainfall data is prepared for this study. Time series data is modelled with a machine learning approach.