基于模糊逻辑和网络层次分析法的森林火险区划

doi:10.13287/j.1001-9332.202402.024

应用生态学报 ›› 2024, Vol. 35 ›› Issue (2): 354-362.doi: 10.13287/j.1001-9332.202402.024

基于模糊逻辑和网络层次分析法的森林火险区划

欧阳逸云^1,2, 苏漳文^1,3, 李春辉^1,2, 曾爱聪^1,2, 郭福涛^1,2*

¹福建农林大学林学院, 福州 350002;
²3S技术与资源优化利用福建省高校重点实验室, 福州 350002;
³漳州职业技术学院, 福建漳州 363000

收稿日期:2023-10-12 修回日期:2023-12-20 出版日期:2024-02-18 发布日期:2024-08-18
通讯作者: ^*E-mail: guofutao@126.com
作者简介:欧阳逸云, 男, 2000年生, 硕士研究生。主要从事林火生态与管理研究。E-mail: oyyy2023@126.com
基金资助:
国家重点研发计划战略性国际科技创新合作重点专项(2018YFE0207800)

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

OUYANG Yiyun^1,2, SU Zhangwen^1,3, LI Chunhui^1,2, ZENG Aicong^1,2, GUO Futao^1,2*

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

摘要/Abstract

摘要： 林火对人类生命财产安全和生态环境产生重要影响,编制高质量的森林火险图对预防林火的发生、指导扑救资源配置、辅助火灾扑灭和支持决策制定均有积极意义。本研究采用基于地理信息系统的多准则决策分析(MCDA)方法以及查阅文献确定影响福建省尤溪县林火发生的主要因子,每个火险因子的重要性利用网络层次分析法(ANP)确定,得出权重,次标准的权重由模糊逻辑评估,使用ArcGIS的聚合函数生成森林火灾风险图,结合卫星火点验证准确度。结果表明: 尤溪县4级及以上火险等级区域占比大,中部和北部林火发生风险较高,该县整体火险情况严峻;模糊ANP模型的准确性较高,达85.8%。引入新的MCDA方法有效地提高了小尺度范围森林火险制图的准确度,可为早期林火预警、扑灭资源规划和分配工作提供依据。

关键词: 森林火灾风险制图, 模糊逻辑, 网络层次分析法(ANP), 基于GIS的多准则决策分析(MCDA)

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)

欧阳逸云, 苏漳文, 李春辉, 曾爱聪, 郭福涛. 基于模糊逻辑和网络层次分析法的森林火险区划[J]. 应用生态学报, 2024, 35(2): 354-362.

OUYANG Yiyun, SU Zhangwen, LI Chunhui, ZENG Aicong, GUO Futao. Forest fire risk zoning based on fuzzy logic and analytical network process[J]. Chinese Journal of Applied Ecology, 2024, 35(2): 354-362.

参考文献

[1] 国家林业局国家发展改革委财政部联合印发《全国森林防火规划(2016—2025年)》. 中国应急管理, 2017(1): 34-44
[2] 国家统计局. 中国统计年鉴. 北京: 中国统计出版社, 2023
[3] Çolak E, Sunar F. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction, 2020, 45: 101479
[4] Sivrikaya F, Küçük Ö. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 2021, 68: 101537
[5] Nuthammachot N, Stratoulias D. Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: Method and case study. Environment, Deve-lopment and Sustainability, 2021, 23: 17443-17458
[6] Guo FT, Zhang LJ, Jin S, et al. Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests, 2016, 7: 250
[7] Zhen Z, Yang S, Wang GY, et al. Evaluation of geographically weighted logistic model and mixed effect model in forest fire prediction in northeast China. Frontiers in Forests and Global Change, 2022, 5: 1040408
[8] Jayshree D, Mahato S, Joshi P, et al. Forest fire susceptibility zonation in Eastern India using statistical and weighted modelling approaches. Remote Sensing, 2023, 15: 1340
[9] Gigović L, Pourghasemi HR, Drobnjak S, et al. Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests, 2019, 10: 408
[10] Sharma LK, Gupta R, Fatima N. Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire. International Journal of Wildland Fire, 2022, 31: 735-758
[11] Meriame M, Romulus C, Firoozeh K, et al. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 2021, 129: 107869
[12] 高超, 林红蕾, 胡海清, 等. 我国林火发生预测模型研究进展. 应用生态学报, 2020, 31(9): 3227-3240
[13] Nikolić G, Vujović F, Golijanin J, et al. Modelling of wildfire susceptibility in different climate zones in Montenegro using GIS-MCDA. Atmosphere, 2023, 14: 929
[14] Eugenio FC, Santos DAR, Fiedler CN, et al. Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. Journal of Environmental Mana-gement, 2016, 173: 65-71
[15] Pourghasemi H, Beheshtirad M, Pradhan B. A compara-tive assessment of prediction capabilities of modified ana-lytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibi-lity mapping. Geomatics, Natural Hazards and Risk, 2016, 7: 861-885
[16] Rivière M, Lenglet J, Noirault A, et al. Mapping territorial vulnerability to wildfires: A participative multi-criteria analysis. Forest Ecology and Management, 2023, 539: 121014
[17] 田晓瑞, 代玄, 王明玉, 等. 多气候情景下中国森林火灾风险评估. 应用生态学报, 2016, 27(3): 769-776
[18] Feizizadeh B, Omarzadeh D, Mohammadnejad V, et al. An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk mode-ling in Gachsaran, Iran. Journal of Environmental Planning and Management, 2023, 66: 1369-1391
[19] Şahiner A, Ermiş T, Karakoyun MH, et al. Determining the most effective way of intervention in forest fires with fuzzy logic modeling: The case of Antalya/Türkiye. Natural Hazards, 2023, 116: 2269-2282
[20] Güngöroğlu C. Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Çakırlar. Human and Ecological Risk Assessment, 2017, 23: 388-406
[21] Cao QQ, Zhang LJ, Su ZW, et al. Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China. Geomatics, Natural Hazards and Risk, 2021, 12: 499-521
[22] Guo FT, Su ZW, Wang GY, et al. Understanding fire drivers and relative impacts in different Chinese forest ecosystems. Science of the Total Environment, 2017, 605: 411-425
[23] Guo FT, Su ZW, Wang GY, et al. Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood. Applied Geography, 2016, 66: 12-21
[24] Malczewski J. GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 2006, 20: 703-726
[25] 张珍, 杨凇, 朱贺, 等. 混合效应模型在林火发生预测中的适用性. 应用生态学报, 2022, 33(6): 1547-1554
[26] 曾爱聪, 蔡奇均, 苏漳文, 等. 基于MODIS卫星火点的浙江省林火季节变化及驱动因子. 应用生态学报, 2020, 31(2): 399-406
[27] Biswas S, Vadrevu KP, Lwin ZM, et al. Factors controlling vegetation fires in protected and non-protected areas of Myanmar. PLoS One, 2015, 10(4): e0124346
[28] Zadeh LA. Fuzzy sets. Information and Control, 1965, 8: 338-353
[29] 王莲芬. 网络分析法(ANP)的理论与算法. 系统工程理论与实践, 2001(3): 44-50
[30] Nachappa TG, Piralilou ST, Gholamnia K, et al. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, 2020, 590: 125275
[31] Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2019, 2: 50
[32] Silva IDB, Valle ME, Barros LC, et al. A wildfire war-ning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing, 2020, 89: 106075
[33] Vadrevu KP, Eaturu A, Badarinath KVS. Fire risk eva-luation using multicriteria analysis: A case study. Environmental Monitoring & Assessment, 2010, 166: 223-239
[34] Neaupane KM, Piantanakulchai M. Analytic network process model for landslide hazard zonation. Engineering Geology, 2006, 85: 281-294
[35] Lan ZG, Su ZW, Guo M, et al. Are climate factors driving the contemporary wildfire occurrence in China? Forests, 2021, 12: 392
[36] Guo FT, Wang GY, Su ZW, et al. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. International Journal of Wildland Fire, 2016, 25: 505-519
[37] Ma WY, Feng ZK, Cheng ZX, et al. Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests, 2020, 11: 507
[38] 蔡奇均, 曾爱聪, 苏漳文, 等. 基于Logistic回归模型的浙江省林火发生驱动因子分析. 西北农林科技大学学报: 自然科学版, 2020, 48(2): 102-109

基于模糊逻辑和网络层次分析法的森林火险区划

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价