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

The importance of monitoring and predicting droughts is underscored by their frequent occurrence in diverse geographical landscapes (Ghozat et al., 2023). Moreover, the likelihood of droughts is expected to increase in the context of global climate change (Xiujia et al., 2022). Their accurate forecasting, however, is a complex problem due to the inherent difficulty in predicting the onset, duration, and cessation of drought events (Mishra and Desai, 2005). This complexity necessitates the development of sophisticated forecasting models that can effectively navigate these challenges. To frame our problem, it is essential to define the prediction target and establish a suitable time horizon for forecasting (Zhang et al., 2019). Given our focus on long-term decision-making, we aim to generate forecasts that extend 12 months into the future. Selecting an appropriate target for drought prediction is more challenging due to its dependence on multiple climatic factors, including temperature and precipitation. Among the various drought severity indices, the Standardized Precipitation Index (SPI) (McKee et al., 1993) and the Palmer Drought Severity Index (PDSI) (Alley, 1984) stand out as fundamental measures.