# A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models

arXiv: https://arxiv.org/abs/2206.08523 · HTML: https://mlsanity.com/paper/2206.08523/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Building high accuracy emulators for scientific simulations with deep neural architecture search](https://mlsanity.com/paper/2001.08055/) (2020)
- [A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources](https://mlsanity.com/paper/2007.12269/) (2020)
- [Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics](https://mlsanity.com/paper/2012.06679/) (2020)
- [Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models](https://mlsanity.com/paper/2005.02328/) (2020)
- [Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning](https://mlsanity.com/paper/2108.11057/) (2021)

## Built on by (most cited first, 9 of 9)

- [Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires](https://mlsanity.com/paper/2212.00970/) (2022)
- [Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark](https://mlsanity.com/paper/2502.12003/) (2025)
- [Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior](https://mlsanity.com/paper/2210.16411/) (2022)
- [Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management](https://mlsanity.com/paper/2310.01593/) (2023)
- [Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning](https://mlsanity.com/paper/2510.09666/) (2025)
- [A Neural Emulator for Uncertainty Estimation of Fire Propagation](https://mlsanity.com/paper/2305.06139/) (2023)
- [Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks](https://mlsanity.com/paper/2408.10271/) (2024)
- [A Real-time Multimodal Transformer Neural Network-powered Wildfire Forecasting System](https://mlsanity.com/paper/2503.05971/) (2025)
- [Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching](https://mlsanity.com/paper/2603.26975/) (2026)

## Abstract (arXiv)

Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbations in conditions can often lead to significant changes in fire spread (such as speed and direction), necessitating a computationally expensive large set of simulations to quantify uncertainty. Model emulation seeks alternative representations of physical models using machine learning, aiming to provide more efficient and/or simplified surrogate models. We propose a dedicated spatio-temporal neural network based framework for model emulation, able to capture the complex behaviour of fire spread models. The proposed approach can approximate forecasts at fine spatial and temporal resolutions that are often challenging for neural network based approaches. Furthermore, the proposed approach is robust even with small training sets, due to novel data augmentation methods. Empirical experiments show good agreement between simulated and emulated firefronts, with an average Jaccard score of 0.76.