Explainable Global Wildfire Prediction Models using Graph Neural
  Networks
Dayou Chen Sibo Cheng Jinwei Hu Matthew Kasoar Rossella Arcucci
Wildfire prediction has become increasingly crucial due to the escalating
impacts of climate change. Traditional CNN-based wildfire prediction models
struggle with handling missing oceanic data and addressing the long-range
dependencies across distant regions in meteorological data. In this paper, we
introduce an innovative Graph Neural Network (GNN)-based model for global
wildfire prediction. We propose a hybrid model that combines the spatial
prowess of Graph Convolutional Networks (GCNs) with the temporal depth of Long
Short-Term Memory (LSTM) networks. Our approach uniquely transforms global
climate and wildfire data into a graph representation, addressing challenges
such as null oceanic data locations and long-range dependencies inherent in
traditional models. Benchmarking against established architectures using an
unseen ensemble of JULES-INFERNO simulations, our model demonstrates superior
predictive accuracy. Furthermore, we emphasise the model's explainability,
unveiling potential wildfire correlation clusters through community detection
and elucidating feature importance via Integrated Gradient analysis. Our
findings not only advance the methodological domain of wildfire prediction but
also underscore the importance of model transparency, offering valuable
insights for stakeholders in wildfire management.