The accurate prediction of drought probability in specific regions is crucial
for informed decision-making in agricultural practices. It is important to make
predictions one year in advance, particularly for long-term decisions. However,
forecasting this probability presents challenges due to the complex interplay
of various factors within the region of interest and neighboring areas. In this
study, we propose an end-to-end solution to address this issue based on various
spatiotemporal neural networks. The models considered focus on predicting the
drought intensity based on the Palmer Drought Severity Index (PDSI) for
subregions of interest, leveraging intrinsic factors and insights from climate
models to enhance drought predictions.
Comparative evaluations demonstrate the superior accuracy of Convolutional
LSTM (ConvLSTM) and transformer models compared to baseline gradient boosting
and logistic regression solutions. The two former models achieved impressive
ROC AUC scores from 0.90 to 0.70 for forecast horizons from one to six months,
outperforming baseline models. The transformer showed superiority for shorter
horizons, while ConvLSTM did so for longer horizons. Thus, we recommend
selecting the models accordingly for long-term drought forecasting.
To ensure the broad applicability of the considered models, we conduct
extensive validation across regions worldwide, considering different
environmental conditions. We also run several ablation and sensitivity studies
to challenge our findings and provide additional information on how to solve
the problem.

我们提出了一种基于各种时空神经网络的端到端解决方案来预测特定地区干旱概率，特别是用于长期决策，同时利用气候模型的内在因素和见解来增强干旱预测，通过比较评估结果表明，卷积 LSTM 和 Transformer 模型在预测干旱强度方面优于基线模型，ROC AUC 分数从 0.90 到 0.70，推荐根据预测时间跨度选择合适的模型进行长期干旱预测。