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arXiv · 2302.02440

A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset

Veronica Wairimu Muriga·Benjamin Rich·Francesco Mauro·Alessandro Sebastianelli·Silvia Liberata Ullo
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Abstract

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.

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Veronica Wairimu Muriga, Benjamin Rich, Francesco Mauro, Alessandro Sebastianelli, Silvia Liberata Ullo. 2023-02-05. A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset. https://arxiv.org/abs/2302.02440

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