Machine Learning-Driven Dynamic Maps Supporting Wildfire Risk Management

被引:0
作者
Perello, Nicole [1 ,2 ]
Meschi, Giorgio [2 ]
Trucchia, Andrea [2 ]
D'Andrea, Mirko [2 ]
Baghino, Francesco [1 ,2 ]
degli Esposti, Silvia [2 ]
Fiorucci, Paolo [2 ]
机构
[1] Univ Genoa, Dept Informat Bioengn Robot & Syst Engn, Via AllOpera Pia 13, I-16145 Genoa, Italy
[2] CIMA Res Fdn, Via A Magliotto 2, I-17100 Savona, Italy
来源
IFAC PAPERSONLINE | 2024年 / 58卷 / 02期
关键词
Wildfire; risk management; machine learning; time series classification;
D O I
10.1016/j.ifacol.2024.07.093
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recent decades have seen an increase in wildfires activity, posing risks to human settlements, and forcing exploration of new technologies for wildfire risk management. Utilizing Machine Learning in Time Series classification, this study produces decision support maps for Civil Protection system in Italy, which is responsible for coordinating national firefighting air fleet. Trained on past events data, the model gives daily indication on wildfire occurrence and aerial support requests for each administrative unit utilizing time series of Forest Fire Danger Rating indexes from RISICO model. Despite its recent implementation, it performed properly in 2023, showcasing model's potential for decision support. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页码:67 / 72
页数:6
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