# Long-term drought prediction using deep neural networks based on geospatial weather data

arXiv: https://arxiv.org/abs/2309.06212 · HTML: https://mlsanity.com/paper/2309.06212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://mlsanity.com/paper/2111.13587/) (2021)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles](https://mlsanity.com/paper/1906.07380/) (2019)
- [Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm](https://mlsanity.com/paper/1607.00567/) (2016)
- [Recurrent Convolutional Neural Networks help to predict location of Earthquakes](https://mlsanity.com/paper/2004.09140/) (2020)
- [Non-contrastive representation learning for intervals from well logs](https://mlsanity.com/paper/2209.14750/) (2022)
- [Tight Risk Bounds for Multi-Class Margin Classifiers](https://mlsanity.com/paper/1507.03040/) (2015)
- [Short-term precipitation prediction using deep learning](https://mlsanity.com/paper/2110.01843/) (2021)
- [Usage of multiple RTL features for Earthquake prediction](https://mlsanity.com/paper/1905.10805/) (2019)

## Built on by (most cited first, 4 of 4)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study](https://mlsanity.com/paper/2310.15912/) (2023)
- [A theoretical framework for self-supervised contrastive learning for continuous dependent data](https://mlsanity.com/paper/2506.09785/) (2025)
- [Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability](https://mlsanity.com/paper/2608.01864/) (2026)

## Abstract (arXiv)

The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due to data complexity and aridity stochasticity. We tackle drought data by introducing an end-to-end approach that adopts a spatio-temporal neural network model with accessible open monthly climate data as the input. Our systematic research employs diverse proposed models and five distinct environmental regions as a testbed to evaluate the efficacy of the Palmer Drought Severity Index (PDSI) prediction. Key aggregated findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts. At the same time, the Convolutional LSTM excels in longer-term forecasting.