Paper Title
Drought Prediction using Machine Learning Algorithm

Abstract
Drought prediction is of critical importance to early warning for drought managements. This work provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climates forcing and initial conditions. SVM model has been applied for classification of real time data obtained from Meteorological department. Here we considered parameters like maximum rainfall, minimum rainfall and precipitation. The prediction is base on the dataset collected for a period of ten years. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Keywords- Drought, Support Vector Machine(SVM),Support Vector Classification (SVC),Confusion Matrix, Machine learning