# Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction

arXiv: https://arxiv.org/abs/2411.09844 · HTML: https://mlsanity.com/paper/2411.09844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 61 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Importance measures derived from random forests: characterisation and extension](https://mlsanity.com/paper/2106.09473/) (2021)

## Built on by (most cited first, 3 of 3)

- [Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model](https://mlsanity.com/paper/2510.03266/) (2025)
- [An Ecologically-Informed Deep Learning Framework for Interpretable and Validatable Habitat Mapping](https://mlsanity.com/paper/2511.17627/) (2025)
- [LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices](https://mlsanity.com/paper/2608.21764/) (2026)

## Abstract (arXiv)

Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire prediction using autoencoders and clustering techniques for anomaly detection. Historical weather and normalised difference vegetation index datasets of Australia for 2005 - 2021 were utilised. Two main unsupervised approaches were analysed. The first used a deep autoencoder to obtain latent features, which were then fed into clustering models, isolation forest, local outlier factor and one-class SVM for anomaly detection. The second approach used a deep autoencoder to reconstruct the input data and use reconstruction errors to identify anomalies. Long Short-Term Memory (LSTM) autoencoders and fully connected (FC) autoencoders were employed in this part, both in an unsupervised way learning only from nominal data. The FC autoencoder outperformed its counterparts, achieving an accuracy of 0.71, an F1-score of 0.74, and an MCC of 0.42. These findings highlight the practicality of this method, as it effectively predicts wildfires in the absence of ground truth, utilising an unsupervised learning technique.