# Australian Bushfire Intelligence with AI-Driven Environmental Analytics

arXiv: https://arxiv.org/abs/2601.06105 · HTML: https://mlsanity.com/paper/2601.06105/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [Smart HPA: A Resource-Efficient Horizontal Pod Auto-scaler for Microservice Architectures](https://mlsanity.com/paper/2403.07909/) (2024)
- [Towards Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures](https://mlsanity.com/paper/2403.15169/) (2024)
- [Robust Partial Least Squares Using Low Rank and Sparse Decomposition](https://mlsanity.com/paper/2407.06936/) (2024)
- [A Survey on Immersive Cyber Situational Awareness Systems](https://mlsanity.com/paper/2408.07456/) (2024)
- [Machine Learning Driven Smishing Detection Framework for Mobile Security](https://mlsanity.com/paper/2412.09641/) (2024)
- [The Future of AI: Exploring the Potential of Large Concept Models](https://mlsanity.com/paper/2501.05487/) (2025)
- [Co-Evolutionary Defence of Active Directory Attack Graphs via GNN-Approximated Dynamic Programming](https://mlsanity.com/paper/2505.11710/) (2025)
- [Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management](https://mlsanity.com/paper/2506.05693/) (2025)
- [Microservice Vulnerability Analysis: A Literature Review with Empirical Insights](https://mlsanity.com/paper/2408.03960/) (2024)
- [SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning](https://mlsanity.com/paper/2510.16474/) (2025)
- [What Skills Do Cyber Security Professionals Need?](https://mlsanity.com/paper/2502.13658/) (2025)
- [Bushfire Severity Modelling and Future Trend Prediction Across Australia: Integrating Remote Sensing and Machine Learning](https://mlsanity.com/paper/2410.02963/) (2024)

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

- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [Comparative Analysis of Large Language Models in Healthcare](https://mlsanity.com/paper/2604.10316/) (2026)

## Abstract (arXiv)

Bushfires are among the most destructive natural hazards in Australia, causing significant ecological, economic, and social damage. Accurate prediction of bushfire intensity is therefore essential for effective disaster preparedness and response. This study examines the predictive capability of spatio-temporal environmental data for identifying high-risk bushfire zones across Australia. We integrated historical fire events from NASA FIRMS, daily meteorological observations from Meteostat, and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) from Google Earth Engine for the period 2015-2023. After harmonizing the datasets using spatial and temporal joins, we evaluated several machine learning models, including Random Forest, XGBoost, LightGBM, a Multi-Layer Perceptron (MLP), and an ensemble classifier. Under a binary classification framework distinguishing 'low' and 'high' fire risk, the ensemble approach achieved an accuracy of 87%. The results demonstrate that combining multi-source environmental features with advanced machine learning techniques can produce reliable bushfire intensity predictions, supporting more informed and timely disaster management.