# Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics

arXiv: https://arxiv.org/abs/2012.06679 · HTML: https://mlsanity.com/paper/2012.06679/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models](https://mlsanity.com/paper/2206.08523/) (2022)
- [Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior](https://mlsanity.com/paper/2210.16411/) (2022)
- [Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting](https://mlsanity.com/paper/2509.25017/) (2025)
- [Anomaly Detection in Satellite Videos using Diffusion Models](https://mlsanity.com/paper/2306.05376/) (2023)
- [Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management](https://mlsanity.com/paper/2310.01593/) (2023)
- [A Neural Emulator for Uncertainty Estimation of Fire Propagation](https://mlsanity.com/paper/2305.06139/) (2023)
- [Wildfire spread forecasting with Deep Learning](https://mlsanity.com/paper/2505.17556/) (2025)
- [An Emulation Framework for Fire Front Spread](https://mlsanity.com/paper/2203.12160/) (2022)
- [N-Tree Diffusion for Long-Horizon Wildfire Risk Forecasting](https://mlsanity.com/paper/2603.07361/) (2026)
- [Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations](https://mlsanity.com/paper/2609.18555/) (2026)

## Abstract (arXiv)

As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, responding to and mitigating the damages caused by these fires. Machine learning techniques provide a potential approach for developing such models. The objective of this study is to evaluate the feasibility of using a Convolutional Long Short-Term Memory (ConvLSTM) recurrent neural network to model the dynamics of wildland fire propagation. The machine learning model is trained on simulated wildfire data generated by a mathematical analogue model. Three simulated datasets are analyzed, each with increasing degrees of complexity. The simplest dataset includes a constant wind direction as a single confounding factor, whereas the most complex dataset includes dynamic wind, complex terrain, spatially varying moisture content and heterogenous vegetation density distributions. We examine how effective the ConvLSTM can learn the fire-spread dynamics over consecutive time steps. It is shown that ConvLSTMs can capture local fire transmission events, as well as the overall fire dynamics, such as the rate at which the fire spreads. Finally, we demonstrate that ConvLSTMs outperform other network architectures that have previously been used to model similar wildland fire dynamics.