用多源环境数据和机器学习预测澳洲火灾高风险区
Australian Bushfire Intelligence with AI-Driven Environmental Analytics
- 融合气象、植被与历史火灾数据，构建时空预测模型
- 集成模型在二分类任务中达到87%准确率
- 适合灾害预警与应急管理决策者使用
澳大利亚的山火是破坏力最强的自然灾害之一，造成重大生态、经济和社会损失。因此，准确预测山火强度对有效防灾减灾至关重要。本研究评估了时空环境数据在识别全澳高风险山火区域方面的预测能力。整合了2015-2023年期间NASA FIRMS的历史火灾事件、Meteostat的每日气象观测数据以及谷歌地球引擎的归一化植被指数（NDVI）等植被指数。通过空间与时间上的数据对齐处理后，评估了随机森林、XGBoost、LightGBM、多层感知机（MLP）及集成分类器等多种机器学习模型。在区分‘低’与‘高’火灾风险的二分类框架下，集成方法取得了87%的准确率。结果表明，结合多源环境特征与先进机器学习技术，可实现可靠的山火强度预测，为更科学及时的灾害管理提供支持。
原文摘要 · Abstract (English)
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.
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