Australian Bushfire Intelligence with AI-Driven Environmental Analytics

TL;DR

This paper demonstrates that integrating diverse environmental data sources with machine learning models can accurately predict high-risk bushfire zones in Australia, aiding disaster preparedness and response.

Contribution

It introduces a multi-source data integration approach combined with ensemble machine learning models for accurate bushfire risk prediction in Australia.

Findings

Ensemble classifier achieved 87% accuracy in predicting fire risk.

Multi-source environmental data improves prediction reliability.

Machine learning models effectively identify high-risk bushfire zones.

Abstract

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.