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Chinese Journal of Applied Ecology ›› 2024, Vol. 35 ›› Issue (2): 354-362.doi: 10.13287/j.1001-9332.202402.024

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Forest fire risk zoning based on fuzzy logic and analytical network process

OUYANG Yiyun1,2, SU Zhangwen1,3, LI Chunhui1,2, ZENG Aicong1,2, GUO Futao1,2*   

  1. 1College of Forestry, Fujian Agricultural and Forestry University, Fuzhou 350002, China;
    23S Technology and Resource Optimization Utilization Key Laboratory of Fujian Universities, Fuzhou 350002, China;
    3Zhangzhou Institute of Technology, Zhangzhou 363000, Fujian, China
  • Received:2023-10-12 Revised:2023-12-20 Online:2024-02-18 Published:2024-08-18

Abstract: Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Key words: forest fire risk mapping, fuzzy logic, analytical network process (ANP), GIS-based multi-criteria decision analysis (MCDA)