Drought Prediction with Raw Satellite Imagery and Ensemble Supervised Machine Learning

Drought is one of the biggest challenges that environmentalists face today because of rapidly evolving climate. The negative impacts of drought on the economy, humans and other living organisms endure long after the ending of drought, and with time its intensity also increases. One way to fight the adverse effects of drought is to perform drought prediction and so that appropriate decisions can be made accordingly. Drought prediction can be made considering vegetation and water level in any region therefore, in this research we are using satellite images to predict drought conditions and its various stages, like if it is about to come or it has passed. All these predictions will be helpful for authorities to make informed decisions. We are employing supervised machine learning nevertheless to obtain the best results. We are using boosting and bagging which is ensemble supervised machine learning techniques. The experiments performed proved that bagging is better than boosting classifiers and it is less computationally expensive; and boosting on the other hand is less accurate and computationally expensive.