A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods.

There is a high need for accurate drought predictions that will play a crucial role to analyze and mitigate the adverse effects of climate change, that will help in ensuring agricultural sustainability and disaster management. Looking for the traditional forecasting methods, they often struggle with very highly complex and nonlinear climate patterns of today's world, earlier there used to be a statistical, normally inclined climate pattern, but due to many hazardous gases in this world and also due to global warming, the climate pattern is very uncertain and very nonlinear. Hence, traditional forecasting methods struggle in computing the climate over a region. Therefore, adoption of advanced computational techniques to explore the application of machine learning with the help of datasets can improve accuracy in predicting droughts. Various ML algorithms that include decision trees, support vector machines, deep learning models and image processing helps analyze effectiveness in predicting variations in temperature, precipitation levels, and severe drought conditions. The dataset in the given paper comprises data from reliable resources, with historical meteorological and environmental parameters. The study mainly focuses on the feature selection and how these have been engineered to calculate new features. It then explains in detailed the hybridization of the new features with the machine learning ensembling classifiers to calculate the drought class labels. The evaluation is done on the basis of accuracy rates, precision score, f1 score and evaluation metrics. The developed Hybrid Drought Forecasting Model shows better results than those reported in previous research. It records better accuracy, precision and f1 scores, proving that our selected set of features and ensemble work is strong. It improves our ability to examine climate trends and sort droughts by their severity.