mlsanityData to 7 Oct 2026

A generative model for surrogates of spatial-temporal wildfire nowcasting

arXiv 2308.02810 ↗First posted 5 Aug 2023cs.LG cs.AI physics.comp-phcited by 8

Not on the map. Why?

Abstract (arXiv)

Recent increase in wildfires worldwide has led to the need for real-time fire nowcasting. Physics-driven models, such as cellular automata and computational fluid dynamics can provide high-fidelity fire spread simulations but they are computationally expensive and time-consuming. Much effort has been put into developing machine learning models for fire prediction. However, these models are often region-specific and require a substantial quantity of simulation data for training purpose. This results in a significant amount of computational effort for different ecoregions. In this work, a generative model is proposed using a three-dimensional Vector-Quantized Variational Autoencoders to generate spatial-temporal sequences of unseen wildfire burned areas in a given ecoregion. The model is tested in the ecoregion of a recent massive wildfire event in California, known as the Chimney fire. Numerical results show that the model succeed in generating coherent and structured fire scenarios, taking into account the impact from geophysical variables, such as vegetation and slope. Generated data are also used to train a surrogate model for predicting wildfire dissemination, which has been tested on both simulation data and the real Chimney fire event.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 48 references that are in the map. First posted on arXiv 5 Aug 2023.

Builds on

12 of 48 references.

References · 12

  1. 2017Neural Discrete Representation Learningcited by 4,650
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  4. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  5. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
  6. 2016Sequential Neural Models with Stochastic Layerscited by 115
  7. 2020Transformers for Modeling Physical Systemscited by 105
  8. 2020A review of machine learning applications in wildfire science and managementcited by 64
  9. 2018Physics-Based Generative Adversarial Models for Image Restoration and Beyondcited by 37
  10. 2022Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Modelscited by 24
  11. 2020An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modellingcited by 10
  12. 2020Data Assimilation in the Latent Space of a Neural Networkcited by 9

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 5.

8 papers.

  1. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  2. 2025Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Datacited by 4
  3. 2025SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence eracited by 2
  4. 2023Estimating fire Duration using regression methodscited by 1
  5. 2023SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecastingcited by 1
  6. 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learningcited by 1
  7. 2024Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing datacited by 0
  8. 2025Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Networkcited by 0