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

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.