Deep Learning-Oriented c-GAN Models for Vegetative Drought Prediction on Peninsular India

In this article, the vegetative drought prediction employing Deep Learning (DL) models is designed, incorporating rainfall data and NOAA satellite-data-derived Vegetation Health Index (VHI) values spanning 1981–2022. Correspondingly, two DL-oriented models based on Generative Adversarial Networks (GANs): 1) Pix2Pix GAN (P2P) and 2) Bidirectional Convolutional LSTM (BiConvLSTM)-P2P GAN (BiCP2P) are developed over the targeted Region of Interest (ROIs). The assimilation of generative DL models for the application of drought forecasting constitutes a novel investigation and a state-of-the-art approach targeted in this work. Subsequently, the primary ROI designated is peninsular India, and the models. efficacy is validated by implementing it on two more ROIs: the Karnataka and Rajasthan states of India. The proposed models. outcomes are compared with several preferred methodologies quantitatively through Coefficient of Determination (R2 score), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and qualitatively employing drought maps denoting the VHI-based drought severity levels over the ROI. Remarkably, excellent performance is demonstrated by the proposed models over peninsular India, with earned R2 score, MSE, and MAE values of 0.971, 0.0016, and 0.020 for P2P and 0.963, 0.0021, and 0.0239 for BiCP2P, respectively. Moreover, generated drought maps efficiently portrayed the drought severities across the land cover and could potentially be extended further for rapid drought risk assessments. The proposed models functioned outstandingly for the developed datasets on the ROIs, corroborating their potential for similar forecasting applications in other climatic zones, which can aid in better planning and preparedness to tackle natural predicaments such as drought calamity.