Multivariate spatio-temporal modeling of drought prediction using graph neural network

Abstract

Drought is a serious natural disaster that causes huge losses to various regions of the world. To effectively cope with this disaster, we need to use drought indices to classify and compare the drought conditions of different regions. We can take appropriate measures according to the category of drought to mitigate the impact of drought. Recently, deep learning models have shown promising results in this domain. However, few of these models consider the relationships between different areas, which limits their ability to capture the complex spatio-temporal dynamics of droughts. In this study, we propose a novel multivariate spatio-temporal sensitive network (MSTSN) for drought prediction, which incorporates both geographical and temporal knowledge in the network and improves its predictive power. We obtained the standardized precipitation evapotranspiration index and meteorological data from the climatic research unit dataset, covering the period from 1961 to 2018. This is the first deep learning method that embeds geographical knowledge in drought prediction. We also provide a solid foundation for comparing our method with other deep learning baselines and evaluating their performance. Experiments show that our method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information. HIGHLIGHTS A novel multivariate spatio-temporal sensitive network (MSTSN) is proposed for drought prediction.; The MSTSN model captures the relationships between different areas by graph neural network.; The MSTSN model extracts the long-term dependencies of data through a gated recurrent unit and enhances its effect by multi-head self-attention.; The MSTSN model predicts the spatial drought distribution closest to the actual distribution.;

Keywords

• deep learning
• drought prediction
• gated recurrent unit
• graph neural networks
• multi-head self-attention
• spatio-temporal sequence data