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

arXiv: https://arxiv.org/abs/2302.02440 · HTML: https://mlsanity.com/paper/2302.02440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping](https://mlsanity.com/paper/2402.00023/) (2024)

## Abstract (arXiv)

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.