A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset
Climate change and increases in drought conditions affect the lives of many
and are closely tied to global agricultural output and livestock production.
This research presents a novel approach utilizing machine learning frameworks
for drought prediction around water basins. Our method focuses on the
next-frame prediction of the Normalized Difference Drought Index (NDDI) by
leveraging the recently developed SEN2DWATER database. We propose and compare
two prediction methods for estimating NDDI values over a specific land area.
Our work makes possible proactive measures that can ensure adequate water
access for drought-affected communities and sustainable agriculture practices
by implementing a proof-of-concept of short and long-term drought prediction of
changes in water resources.