# Deep graphical regression for jointly moderate and extreme Australian wildfires

arXiv: https://arxiv.org/abs/2308.14547 · HTML: https://mlsanity.com/paper/2308.14547/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 113 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth](https://mlsanity.com/paper/2105.04550/) (2021)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk](https://mlsanity.com/paper/2208.07590/) (2022)
- [Advances in Statistical Modeling of Spatial Extremes](https://mlsanity.com/paper/2007.00774/) (2020)
- [Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks](https://mlsanity.com/paper/2208.07581/) (2022)
- [Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires](https://mlsanity.com/paper/2212.00970/) (2022)
- [Spatiotemporal wildfire modeling through point processes with moderate and extreme marks](https://mlsanity.com/paper/2105.08004/) (2021)
- [A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes](https://mlsanity.com/paper/2112.14920/) (2021)
- [An Extreme Value Bayesian Lasso for the Conditional Left and Right Tails](https://mlsanity.com/paper/2010.07164/) (2020)
- [Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning](https://mlsanity.com/paper/2212.01796/) (2022)
- [Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data](https://mlsanity.com/paper/2212.11367/) (2022)
- [Using Echo State Networks to Inform Physical Models for Fire Front Propagation](https://mlsanity.com/paper/2302.04960/) (2023)
- [Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions](https://mlsanity.com/paper/2205.09908/) (2022)
- [Spatial hierarchical modeling of threshold exceedances using rate mixtures](https://mlsanity.com/paper/1912.04571/) (2019)
- [Distributional regression models for Extended Generalized Pareto distributions](https://mlsanity.com/paper/2209.04660/) (2022)
- [Spatial wildfire risk modeling using mixtures of tree-based multivariate Pareto distributions](https://mlsanity.com/paper/2308.03870/) (2023)

## Built on by (most cited first, 8 of 8)

- [Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events](https://mlsanity.com/paper/2404.17652/) (2024)
- [Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks](https://mlsanity.com/paper/2310.02600/) (2023)
- [Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves](https://mlsanity.com/paper/2410.00984/) (2024)
- [Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts](https://mlsanity.com/paper/2407.02125/) (2024)
- [Deep learning joint extremes of metocean variables using the SPAR model](https://mlsanity.com/paper/2412.15808/) (2024)
- [At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition](https://mlsanity.com/paper/2401.14210/) (2024)
- [Towards physics-informed neural networks for landslide prediction](https://mlsanity.com/paper/2407.06785/) (2024)
- [Neural Bayes estimation and selection for complex bivariate extremal dependence models](https://mlsanity.com/paper/2503.23156/) (2025)

## Abstract (arXiv)

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level~1 and~2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalized Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth.