# Predicting spatial distribution of Palmer Drought Severity Index

arXiv: https://arxiv.org/abs/2208.14833 · HTML: https://mlsanity.com/paper/2208.14833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Recurrent Convolutional Neural Networks help to predict location of Earthquakes](https://mlsanity.com/paper/2004.09140/) (2020)
- [Usage of multiple RTL features for Earthquake prediction](https://mlsanity.com/paper/1905.10805/) (2019)

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

- [Correcting sampling biases via importance reweighting for spatial modeling](https://mlsanity.com/paper/2309.04824/) (2023)

## Abstract (arXiv)

The probability of a drought for a particular region is crucial when making decisions related to agriculture. Forecasting this probability is critical for management and challenging at the same time. The prediction model should consider multiple factors with complex relationships across the region of interest and neighbouring regions. We approach this problem by presenting an end-to-end solution based on a spatio-temporal neural network. The model predicts the Palmer Drought Severity Index (PDSI) for subregions of interest. Predictions by climate models provide an additional source of knowledge of the model leading to more accurate drought predictions. Our model has better accuracy than baseline Gradient boosting solutions, as the R2 score for it is 0.90 compared to 0.85 for Gradient boosting. Specific attention is on the range of applicability of the model. We examine various regions across the globe to validate them under different conditions. We complement the results with an analysis of how future climate changes for different scenarios affect the PDSI and how our model can help to make better decisions and more sustainable economics.