Ensemble Learning with CNN and BMO for Drought Prediction

Natural and complex climate disasters like drought have a number of underlying causes that are observed over timescales ranging from months to years. Sustaining natural resources for farming necessitate drought management plans wherein drought prediction is becoming powerful and flexible with intelligent techniques. It has been proved that machine learning and deep learning techniques are successful for drought prediction. Usage of ensemble hybrid intelligent learning algorithm is available for groundwater and gully erosion modeling but rarely emphasized for drought prediction in the literature. This paper discusses ensemble of Convolutional Neural Network (CNN) and Barnacles Mating Optimizer (BMO) to enhance the efficiency of a CNN model for drought prediction. The input for the proposed ensemble learning model includes the indices Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI) and Enhanced Vegetation Index (EVI), which are calculated from satellite data taken for Kolar regions of Karnataka. predicted drought is classified into low drought, moderate drought and severe drought, based on the NDVI value. Improved results are observed.