The study evaluates deep learning surrogates for wildfire spread prediction, training four architectures on 10,584 high‑resolution simulations from Catalonia. Results show that only surface fuel load significantly predicts burn probability, and convolutional models mainly use distance to the fire front while a transformer model emphasizes fuel and terrain. When applied to a new region without retraining, the models still perform reasonably, with only a modest accuracy drop.
By Marcin Lawenda, Aleksandra Krasicka, David Caballero, Luis Torres, {\L}ukasz Szustak
WildfireSpreadBench evaluates machine‑learning models for predicting next‑day wildfire spread, comparing five discriminative and one generative architecture on the WildfireSpreadTS dataset. The study shows that model rankings differ markedly when using Average Precision versus threshold‑dependent metrics such as F1 and IoU, revealing three distinct prediction profiles—over‑predicting, balanced, and under‑predicting—that AP alone cannot distinguish. Expanding input channels modestly affected AP, underscoring that AP may favor models with predictions poorly suited for operational use.
By Arin Gopakumar, Marco Pannozzo
arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.
By Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis
arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.
By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu
The paper introduces an AI surrogate modelling framework that accelerates wildfire hazard mapping across Canada. By training a multi‑task deep learning model on national‑scale data, the surrogate reproduces key outputs of the computationally intensive BurnP3+ simulator—burn probability, mean fire intensity, and mean rate of spread—in seconds instead of days or weeks. The framework includes standardized training, fine‑tuning, and evaluation protocols, domain‑specific metrics, and counterfactual evaluation to ensure robust out‑of‑distribution performance, achieving up to 806‑fold CPU speed‑up and 13% memory usage compared to the original simulator.
By Hager Radi Abdelwahed, Toluwaleke Olutayo, Chirag Goel, Jana Osea, Aldo Zaimi, Tom Swystun, Sandy Erni, Francisco Emiliano Lopez Saavedra, Yan Boulanger, Ga\'etan Marceau Caron
The paper presents a spatiotemporal graph neural network that predicts global fire radiative power (FRP) up to seven days ahead using recent observations and reanalysis data. Trained on 2020‑2022 data and evaluated for 2023‑2024, the model reproduces the seasonal FRP cycle and outperforms persistence, reducing mean squared error by 32–43% at one‑day lead and 24–40% at seven days at 0.1° resolution. While large fires are reliably detected, the model underestimates their radiative power and faces challenges in intensity calibration and small‑fire placement.
By Li Zhang, Jun Wang, Isidora Jankov, Yongxin Liu, Gonzalo A. Ferrada, Ravan Ahmadov, Ligia Bernardet, Haonan Chen, Shobha Kondragunta