Drought Detection in India using Spatio-Temporal Graph Neural Networks

Droughts, which are defined by extended periods of water scarcity, offer significant difficulties to agriculture, ecosystems, and human populations. Drought detection that requires timely and precise assessment is critical for effective mitigation and resource planning. This work proposes a novel technique for drought detection using satellite imagery with the capabilities of Graph Neural Networks (GNNs). The proposed GNN-based model captures spatio-temporal dependencies by representing 671 districts across India as nodes, connected based on geographical proximity. The spatio-temporal model achieved its best performance with an RMSE of 6.849, MAE of 4.367, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.903 for the Normalized Vegetation Supply Water Index (NVSWI). This work is one of the initial attempts to predict the drought over the Indian region using graph neural networks.