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Fire danger forecasting using machine learning-based models and meteorological observation: a case study in Northeastern China

  • 1229: Multimedia Data Analysis for Smart City Environment Safety
  • Published:
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Abstract

Wildfire is one of the primary natural disturbance agents in the forests of China. The forecast of fire danger is critically important to assist stakeholders to avoid and mitigate wildfire-induced hazards and losses to both human society and natural ecosystems. Currently, fire danger rating methods often focus on fire weather classification based on fixed thresholds, which has shortcomings in generalizability and robustness. Based on historical fire occurrence data and meteorological data of Northeastern China from 2004 to 2015, we proposed a forest fire danger rating classification and forecasting model by combining the advantages of the Canadian Fire Weather Index (FWI) system and two machine learning models such as the Long Short-Term Memory (LSTM) network and Random Forest (RF) model. The method is divided into two stages. The first stage is the LSTM-based FWI system indexes prediction. In the first stage, the future FWI system indexes are obtained through the LSTM-based prediction model, and the RMSE and MAE of the prediction results are calculated to verify the prediction performance of the model. The second stage is random forest-based fire danger rating prediction method. In the second stage, we use the random forest method to get the fire danger occurrence probability and present the fire danger rating classification scheme. Then we verify the reliability of the fire danger rating classification scheme by using the forest fire danger data in Qipan Mountain. Our method predicts two randomly selected future intervals, and the prediction accuracy is 87.5%. The experimental results show that our machine learning-based forest fire danger rating classification method can provide a new idea for forest fire danger warnings.

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Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was financially supported by the Key Project of National Natural Science Foundation of China (U1908212), the National Key R&D Program of China (2017YFA0604403), the National Natural Science Foundation of China (32071583), the Young and Middle-Aged Scientific and Technological Innovation Talent Support Program of Shenyang (RC210081), the Local Science and Technology Development Fund Project Under the Guidance of the Central Government of China (1653137155953), and the Taking Lead Science and Technology Research Project of Liaoning (2021jh1/10400006).

Zhenyu Chen and Chen Zhang contributed equally to this work, and should be considered co-first authors.

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Authors and Affiliations

  1. Northeastern University, Shenyang, 110819, People’s Republic of China

    Zhenyu Chen, Chen Zhang, Wendi Li, Lanyu Gao, Liming Liu & Changsheng Zhang

  2. Environment Research Institute, Shandong University, Qingdao, 266237, People’s Republic of China

    Lei Fang

  3. CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, People’s Republic of China

    Lei Fang

  4. Big Data Research Center for Ecology and Environment, Shandong University, Qingdao, 266237, People’s Republic of China

    Lei Fang

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  1. Zhenyu Chen
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Correspondence to Lei Fang or Changsheng Zhang.

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Chen, Z., Zhang, C., Li, W. et al. Fire danger forecasting using machine learning-based models and meteorological observation: a case study in Northeastern China. Multimed Tools Appl 83, 61861–61881 (2024). https://doi.org/10.1007/s11042-023-15881-1

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  • DOI: https://doi.org/10.1007/s11042-023-15881-1

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