IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6292-d1117346.html
   My bibliography  Save this article

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Author

Listed:
  • Chaoxue Tan

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China)

  • Zhongke Feng

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China
    Intelligent Forestry Key Laboratory of Haikou City, School of Forestry, Hainan University, Haikou 570228, China)

Abstract

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan.

Suggested Citation

  • Chaoxue Tan & Zhongke Feng, 2023. "Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6292-:d:1117346
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6292/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6292/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinghu Pan & Weiguo Wang & Junfeng Li, 2016. "Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1879-1899, April.
    2. Zhangwen Su & Lujia Zheng & Sisheng Luo & Mulualem Tigabu & Futao Guo, 2021. "Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1317-1345, August.
    3. Amatulli, Giuseppe & Peréz-Cabello, Fernando & de la Riva, Juan, 2007. "Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty," Ecological Modelling, Elsevier, vol. 200(3), pages 321-333.
    4. Ali Nouh Mabdeh & A’kif Al-Fugara & Khaled Mohamed Khedher & Muhammed Mabdeh & Abdel Rahman Al-Shabeeb & Rida Al-Adamat, 2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
    5. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    6. Sofia Bajocco & Eleni Dragoz & Ioannis Gitas & Daniela Smiraglia & Luca Salvati & Carlo Ricotta, 2015. "Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    3. Kuter, Semih & Usul, Nurunnisa & Kuter, Nazan, 2011. "Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale," Ecological Modelling, Elsevier, vol. 222(17), pages 3033-3040.
    4. Rosanna Salvia & Valentina Quaranta & Adele Sateriano & Giovanni Quaranta, 2022. "Land Resource Depletion, Regional Disparities, and the Claim for a Renewed ‘Sustainability Thinking’ under Early Desertification Conditions," Resources, MDPI, vol. 11(3), pages 1-14, March.
    5. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    7. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-5, Center for Economic Studies, U.S. Census Bureau.
    8. Luca Salvati & Ilaria Tombolini & Roberta Gemmiti & Margherita Carlucci & Sofia Bajocco & Luigi Perini & Agostino Ferrara & Andrea Colantoni, 2017. "Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
    9. Vito Imbrenda & Rosa Coluzzi & Francesca Mariani & Bogdana Nosova & Eva Cudlinova & Rosanna Salvia & Giovanni Quaranta & Luca Salvati & Maria Lanfredi, 2023. "Working in (Slow) Progress: Socio-Environmental and Economic Dynamics in the Forestry Sector and the Contribution to Sustainable Development in Europe," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    10. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Benavent-Corai, J. & Rojo, C. & Suárez-Torres, J. & Velasco-García, L., 2007. "Scaling properties in forest fire sequences: The human role in the order of nature," Ecological Modelling, Elsevier, vol. 205(3), pages 336-342.
    12. Maria Lanfredi & Rosa Coluzzi & Vito Imbrenda & Bogdana Nosova & Massimiliano Giacalone & Rosario Turco & Marcela Prokopovà & Luca Salvati, 2023. "In-between Environmental Sustainability and Economic Viability: An Analysis of the State, Regulations, and Future of Italian Forestry Sector," Land, MDPI, vol. 12(5), pages 1-21, May.
    13. Liu, Shilei & Xu, Jintao, 2022. "Wildfire, protected areas and forest ownership: The case of China," Land Use Policy, Elsevier, vol. 122(C).
    14. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 305-327, October.
    15. Khakzad, Nima & Cozzani, Valerio, 2020. "Special issue: Quantitative assessment and risk management of Natech accidents," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    16. Katarzyna Kopczewska, 2022. "Spatial machine learning: new opportunities for regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 713-755, June.
    17. Rares Halbac-Cotoara-Zamfir & Gloria Polinesi & Francesco Chelli & Luca Salvati & Leonardo Bianchini & Alvaro Marucci & Andrea Colantoni, 2022. "Found in Complexity, Lost in Fragmentation: Putting Soil Degradation in a Landscape Ecology Perspective," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    18. Diana Mancilla-Ruiz & Francisco de la Barrera & Sergio González & Ana Huaico, 2021. "The Effects of a Megafire on Ecosystem Services and the Pace of Landscape Recovery," Land, MDPI, vol. 10(12), pages 1-16, December.
    19. Kwadwo YEBOAH BOTAH, 2023. "Forest Fires In A Changing Climate: Risk Assessment And Management In Leiria National Forest, Portugal," Eastern European Journal for Regional Studies (EEJRS), Center for Studies in European Integration (CSEI), Academy of Economic Studies of Moldova (ASEM), vol. 9(2), pages 169-191, December.
    20. Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6292-:d:1117346. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.