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Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for Imbalanced Data

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Abstract

Since the 1953 truce, the Republic of Korea Army (ROKA) has regularly conducted artillery training, posing a risk of wildfires — a threat to both the environment and the public perception of national defense. To assess this risk and aid decision-making within the ROKA, we built a predictive model of wildfires triggered by artillery training. To this end, we combined the ROKA dataset with meteorological database. Given the infrequent occurrence of wildfires (imbalance ratio \(\approx \) 1:24 in our dataset), achieving balanced detection of wildfire occurrences and non-occurrences is challenging. Our approach combines a weighted support vector machine with a Gaussian mixture-based oversampling, effectively penalizing misclassification of the wildfires. Applied to our dataset, our method outperforms traditional algorithms (G-mean=0.864, sensitivity=0.956, specificity= 0.781), indicating balanced detection. This study not only helps reduce wildfires during artillery trainings but also provides a practical wildfire prediction method for similar climates worldwide.

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Data Availability

While some of the data employed in the study is accessible at the following URL: https://github.com/jihyun-nam/Prediction-of-Forest-Fire-Risk, it is important to note that obtaining the complete dataset necessitates permission from the Republic of Korea Army. Therefore, kindly request the author to acquire the necessary permissions for the entire dataset.

Code Availability

The codes utilized in this paper can be accessed through the following URL: https://github.com/jihyun-nam/Prediction-of-Forest-Fire-Risk.

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Funding

Ji Hyun Nam was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government Ministry of Science and ICT (MSIT) (RS-2023-00278691). Seongil Jo was supported by Basic Science Research Program through the NRF funded by the Korea government (MSIT) (RS-2023-00209229). Jaeoh Kim was supported by the NRF Grant through the Korea Government (MSIT) under Grant NRF-2022R1A5A7033499.

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Authors and Affiliations

  1. Department of Statistics and Data Science, Inha University, Incheon, South Korea

    Ji Hyun Nam, Seongil Jo & Jaeoh Kim

  2. Marshall School of Business, University of Southern California, Los Angeles, USA

    Jongmin Mun

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J.H. Nam and J. Mun contributed equally to this work, while both S. Jo and J. Kim supervised the findings as corresponding authors.

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Correspondence to Jaeoh Kim.

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Ji Hyun Nam and Jongmin Mun are the first authors of this paper. Jaeoh Kim is the corresponding author and Seongil Jo is the Co-corresponding author of this paper.

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Nam, J.H., Mun, J., Jo, S. et al. Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for Imbalanced Data. J Classif 41, 170–189 (2024). https://doi.org/10.1007/s00357-024-09467-1

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