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.
@article{arxiv.2302.02440,
title = {A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset},
author = {Veronica Wairimu Muriga and Benjamin Rich and Francesco Mauro and Alessandro Sebastianelli and Silvia Liberata Ullo},
journal= {arXiv preprint arXiv:2302.02440},
year = {2023}
}