Intelligent Hybrid Model for Drought Assessment coupled with Bio-inspired Techniques

Drought globally impacts many sectors, importantly agriculture and ecosystems. Drought impact mitigation is an immediate need, which can be achieved through reliable drought monitoring/prediction and early warning. Analyzing past patterns and predicting future drought conditions can significantly reduce the impact of drought on individuals, communities, and the environment. In this paper, the intelligent hybrid model for drought assessment is proposed. The proposed hybrid model integrates Convolutional Neural Network (CNN) coupled with a Sparrow Search Algorithm (SSA) for observing improvement in prediction efficiency. Various indices such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Atmospherically Resistant Vegetation Index (ARVI) are calculated for the drought assessment model using the satellite data from the Kolar region of Karnataka, India. The accuracy for the standalone CNN model is 91% whereas the accuracy of the CNN when coupled with SSA improved to 94%. The time taken to run the hybrid model also improved immensely. The model is also used to classify the predicted severity of drought into low, medium and severe based on the NDVI values computed.