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
相关论文
共 50 条
  • [21] Machine Learning-Driven Approaches for Advanced Microwave Filter Design
    Javadi, Sara
    Rezaee, Behrooz
    Nabavi, Sayyid Shahab
    Gadringer, Michael Ernst
    Boesch, Wolfgang
    ELECTRONICS, 2025, 14 (02):
  • [22] Machine learning-driven process of alumina ceramics laser machining
    Behbahani, Razyeh
    Sarvestani, Hamidreza Yazdani
    Fatehi, Erfan
    Kiyani, Elham
    Ashrafi, Behnam
    Karttunen, Mikko
    Rahmat, Meysam
    PHYSICA SCRIPTA, 2023, 98 (01)
  • [23] An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers
    Fanidis, Dionysios
    Pezoulas, Vasileios C.
    Fotiadis, Dimitrios, I
    Aidinis, Vassilis
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 2305 - 2315
  • [24] Machine learning-driven dynamic risk prediction for highly pathogenic avian influenza at poultry farms in Republic of Korea: Daily risk estimation for individual premises
    Yoo, Dae-sung
    Song, Yu-han
    Choi, Dae-woo
    Lim, Jun-Sik
    Lee, Kwangnyeong
    Kang, Taehun
    TRANSBOUNDARY AND EMERGING DISEASES, 2022, 69 (05) : 2667 - 2681
  • [25] Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis
    Akshay, Akshay
    Katoch, Mitali
    Shekarchizadeh, Navid
    Abedi, Masoud
    Sharma, Ankush
    Burkhard, Fiona C.
    Adam, Rosalyn M.
    Monastyrskaya, Katia
    Gheinani, Ali Hashemi
    GIGASCIENCE, 2024, 13
  • [26] Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
    Ng, Hibatul Azizi Hisyam
    Mahmoodi, Toktam
    BIG DATA AND COGNITIVE COMPUTING, 2024, 8 (12)
  • [27] AI Meets the Eye of the Storm: Machine Learning-Driven Insights for Hurricane Damage Risk Assessment in Florida
    Arachchige, Sameera Maha
    Pradhan, Biswajeet
    EARTH SYSTEMS AND ENVIRONMENT, 2025,
  • [28] Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils
    Bi, Zihan
    Sun, Jian
    Xie, Yutong
    Gu, Yilu
    Zhang, Hongzhen
    Zheng, Bowen
    Ou, Rongtao
    Liu, Gaoyuan
    Li, Lei
    Peng, Xuya
    Gao, Xiaofeng
    Wei, Nan
    JOURNAL OF HAZARDOUS MATERIALS, 2024, 476
  • [29] A Layered Quality Framework for Machine Learning-driven Data and Information Models
    Azimi, Shelernaz
    Pahl, Claus
    PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS), VOL 1, 2020, : 579 - 587
  • [30] Machine Learning-Driven Virtual Bidding With Electricity Market Efficiency Analysis
    Li, Yinglun
    Yu, Nanpeng
    Wang, Wei
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2022, 37 (01) : 354 - 364