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Volume 9, Issue 1 (3-2023)
jhehp 2023, 9(1): 33-39 |
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Bozorgmer B, Macki Aleagha M, Behbahani Nia A. Identification and Assessment of Fire Risk Factors Using the Delphi Technique and Multiple-Criteria Decision Analysis A Case Study: Forest Parks in the Southern Slopes of Alborz, Iran. jhehp 2023; 9 (1) :33-39
URL: http://jhehp.zums.ac.ir/article-1-556-en.html
URL: http://jhehp.zums.ac.ir/article-1-556-en.html
1- Department of Environment, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
Abstract: (2512 Views)
Background: The present research was conducted to achieve sustainable development and optimal use of environmental resources for identifying and prioritizing fire risk factors and fire management in Vesiye forest park located on the southern slopes of Alborz, Iran, to control and reduce the resultant destruction.
Methods: The three-round Delphi questionnaire and previous information were used to identify the most important fire risk factors in the studied forest park, which were subsequently ranked and weighted using Analytic Hierarchy Process (AHP).
Results: The effective factors in the fire were identified to be vegetation type, slope, slope direction, distance to roads, distance to settlements, presence of tourists, altitude changes in the region, annual temperature changes, vegetation density, land use, atmospheric humidity percentage, wind speed, direction and the presence of hunters, and animal husbandry, respectively. According to the analyzed data, the highest ranks were assigned to vegetation type (0.2785), slope (0.1875), slope direction (0.1495), distance to roads (0.1075), and distance to settlements (0.0665), and the lowest weight to height (0.055).
Conclusion: The results revealed that the vegetation type has provided suitable conditions for fire in Vesiye Forest Park due to its high flammability factor as a fuel for the fire and the distance to roads as the factor of easy access for tourists to the forest park.
Methods: The three-round Delphi questionnaire and previous information were used to identify the most important fire risk factors in the studied forest park, which were subsequently ranked and weighted using Analytic Hierarchy Process (AHP).
Results: The effective factors in the fire were identified to be vegetation type, slope, slope direction, distance to roads, distance to settlements, presence of tourists, altitude changes in the region, annual temperature changes, vegetation density, land use, atmospheric humidity percentage, wind speed, direction and the presence of hunters, and animal husbandry, respectively. According to the analyzed data, the highest ranks were assigned to vegetation type (0.2785), slope (0.1875), slope direction (0.1495), distance to roads (0.1075), and distance to settlements (0.0665), and the lowest weight to height (0.055).
Conclusion: The results revealed that the vegetation type has provided suitable conditions for fire in Vesiye Forest Park due to its high flammability factor as a fuel for the fire and the distance to roads as the factor of easy access for tourists to the forest park.
Type of Study: Original Article |
Subject:
Environmental Health, Sciences, and Engineering
Received: 2023/01/16 | Accepted: 2023/02/16 | Published: 2023/03/11
Received: 2023/01/16 | Accepted: 2023/02/16 | Published: 2023/03/11
References
1. Behzadi H, Mohtashamnia S, Gharehdaghi H. Rangelands and Forests Fire Risk Zoning Using GIS and AHP Model (Case Study: Bamu National Park, Fars Province). Iran J Range Desert Res. 2018; 25(4): 817-28. [Google Scholar]
2. Babaie Kafaki S, Moradzadeh Azar N, Kiadaliri H, Banj Shafiei A, Rahimizadeh N. Fire Risk Mapping Using Hierarchical Analysis and GIS Methods Along with Risk Assessment in the Zab-Sardasht Forests. J Renew Nat Resour Res. 2021; 11(2): 101-15. [Google Scholar]
3. Progias P, Sirakoulis GC. An FPGA Processor for Modeling Wildfire Spreading. Math Comput Model. 2013; 57: 1436-52. [Crossref] [Google Scholar]
4. Richardson LA, Champ PA, Loomis JB. The Hidden Cost of Wildfires: Economic Valuation of Health Effects of Wildfire Smoke Exposure in Southern California. J For Econ. 2012; 18(1): 14-35. [Crossref] [Google Scholar]
5. Smith HG, Blake WH, Owens PN. Discriminating Fine Sediment Sources and the Application of Sediment Tracers in Burned Catchments: a Review. Hydrol Process. 2013; 27(6): 943-58. [Crossref] [Google Scholar]
6. Pourtaghi ZS, Pourghasemi HR, Rossi M. Forest Fire Susceptibility Mapping in the Minudasht Forests, Golestan Province, Iran. Environ Earth Sci. 2015; 73: 1515-33. [Crossref] [Google Scholar]
7. Hernandez-Leal PA, Arbelo M. Fire Risk Assessment Using Satellite Data. Adv Space Res. 2006; 37: 741-6. [Crossref] [Google Scholar]
8. Martin RM, Kneeland D, Brooks D, Matta R. State of the World’s Forests. Rome: Food and Agriculture Organization of the United Nations (FAO). 2012. [Article]
9. Adab H, Kanniah KD, Solaimani K. Modeling Forest Fire Risk in the Northeast of Iran Using Remote Sensing and GIS Techniques. Nat Hazards. 2013; 65: 1723-43. [Crossref] [Google Scholar]
10. Chowdhury EH, Hassan QK. Use of Remote Sensing-Derived Variables in Developing a Forest Fire Danger Forecasting System. Nat Hazards. 2013; 67: 321-34. [Crossref] [Google Scholar]
11. Werf GR, Van Der Randerson JT, Collatz GJ, Giglio L, Kasibhatla PS, Arellano Jr AF, et al. Continental-Scale Partitioning of Fire Emissions During the 1997 to 2001 El Nino/La Nina Period. Sci. 2004; 303: 73-7. [Crossref] [Google Scholar]
12. Bowman D, Balch J, Artaxo P, Bond W, Carlson J, Cochrane M, et al. Fire in the Earth System. Sci. 2009; 324: 481-4. [Crossref] [Google Scholar]
13. Certini G. Effects of Fire on Properties of Forest Soils: a Review. Oecologia. 2005; 143: 1-10. [Crossref] [Google Scholar]
14. Akther MS, Hassan QK. Remote Sensing-Based Assessment of Fire Danger Conditions Over Boreal Forest. IEEE J Sel Top Appl Earth Obs Remote Sens. 2011; 4: 992-9. [Crossref] [Google Scholar]
15. Barrantes G. Economic Valuation of Water Supply as a Key Environmental Service Provided by Montane Oak Forest Watershed Areas in Costa Rica. Berlin. Ecol Conserv Neotrop Mont Oak For. 2006; 435-46. [Crossref] [Google Scholar]
16. Ojea E, Martin-Ortega J, Chiabai A. Defining and Classifying Ecosystem Services for Economic Valuation: the Case of Forest Water Services. Environ Sci Policy. 2012; 19(20): 1-15. [Crossref] [Google Scholar]
17. Srivastava S, Saran S, Deby R, Dadhwal VK. A Geo-Information System Approach for Forest Fire Likelihood Based on Causative and Anti-Causative Factors. Int J Geogr Inf Sci (IJGIS). 2013. [Crossref] [Google Scholar]
18. Sekhavati E, Jalilzadeh R. Optimizing the Risk of Building Environments Using Multi-Criteria Decision Making. Anthropogenic Pollut. 2022; 6(1): 1-7. [Google Scholar]
20. Gorte W, Bracmort K. Forest Fire/Wildfire Protection. Congr Res Serv. 2012; 27. [Google Scholar]
21. Huyen DT, Tuan VA. Applying GIS and Multi-Criteria Evaluation in Forest Fire Risk Zoning in Son La Province, Vietnam. Int Symp Geoinfor Spat Infrastruct Dev Earth Allied Sci. 2008.
22. Arianoutsou M, Koukoulas S, Kazanis D. Evaluation Post-Fire Forest Resilience Using GIS and Multi-Criteria Analysis. Environ Manage. 2011; 47: 384-97. [Crossref] [PubMed]
23. Mohammadi F, Shabanian N, Pourhashemi M, Fatehi P. Forest Fire Risk Mapping Using GIS and AHP in Pave Forests. Iran J For Pop Res. 2010; 18(4): 569-86. [Article]
24. Salamati H, Mostafa Lou H, Mastoori A, Honardoust F. Assessment and Mapping Forest Fire Risk Using GIS in Golestan Province Forests. Abstracts of 1st International Conference on Wildfire in Natural Resources Lands, Iran. 2011; 10.
25. Mahdavi A. Forests and Rangelands? Wildfire Risk Zoning Using GIS and AHP Techniques. Casp J Environ Sci. 2012; 10(1): 43-52. [Google Scholar]
26. Fataei E, Torabian A, Hosseinzadeh Kalkhoran M, Alighadri M, Hosseinzadeh S. Selection of Optimum Municipal Wastewater Treatment Process Using AHP (Case Study: Ardebil, Tabriz, and Uremia). J Health Hyg. 2013; 4(3): 260-72. [Google Scholar]
27. Nami D, Fataei E, Nejaei A, Zaeimdar M. Evaluation of Environmental Potential in Parsabad, Moghan for Urban Development Using GIS and AHP. J Environ Sci Technol. 2017; 19(5): 475-86. [Google Scholar]
28. Fataei E. Feasibility Study of Border Industrial Town Using AHP and TOPSIS. Geogr Dev: Iran J. 2015; 12(37): 181-94. [Google Scholar]
29. Fataei E, Safavian ST. Comparative Study on the Efficiency of ANP and PROMETHEE Methods in Locating MSW Landfill Sites. Anthropogenic Pollut. 2017; 1(1): 40-5. [Google Scholar]
30. Zare Kendazi S, Jafari R, Mousavi SA, Sultan YS, Dehghan Farsi S, Arabi K. Fire Risk Zoning in Natural Lands of Marvdasht City Using Analytic Hierarchy Process and Geographic Information System. The 1st International Conference on Geographical Information System, Silk Road, Isfahan, Iran. 2017. [Article]
31. Ajin RS, Loghin AM, Vinod PG, Jacob MK. Forest Fire Risk Zone Mapping Using RS and GIS Techniques: A Study in Achankovil Forest Division, Kerala, India. J Earth, Environ Health Sci. 2016; 2(3): 109-15. [Crossref] [Google Scholar]
32. Chavan M, Das KK, Suryawanshi R. Forest Fire Risk Zonation Using Remote Sensing and GIS in Huynial Watershed, Tehri Garhwal District, UA. Int J Basic Appl Res. 2012; 2(7): 6-12. [Google Scholar]
33. Chuvieco E, Congalton R. Application of Remote Sensing and Geographic Information Systems to Forest Fire Hazard Mapping. Remote Sens Environ. 1989; 29: 147-59. [Crossref] [Google Scholar]
34. Bagherabadi R, Shikhkanloo Milan F, Zarei Mohammadabad M. Evaluation of Fire Risk in the Zagros Forests (Case Study: Dalahu County). J Ecosyst Manag . 2022; 1(3): 60-72. [Article] [DOI]
35. Ghasemi Aghbash F, Fataei E. The Study of the Effect of Forest Management on the Biodiversity of Woody Species in Fandoghloo Forest. Pajouhesh-va-Sazandegi. 2006; 19(2): 11-8. (In Persian(. [Article]
36. Dong X, Guofan S, Limin D, Zhanqing H, Lei T, Hui W. Mapping Forest Fire Risk Zones with Spatial Data and Principal Component Analysis. Sci China Ser E: Technol Sci. 2006; 49(1): 140-9. [Crossref] [Google Scholar]
37. Fataei E. Biomass and Carbon Sequestration of Native Forests in the Northwest of Iran. Anthropogenic Pollut. 2020; 4(2): 52-67. [Google Scholar]
38. Hong H, Naghibi SA, Moradi Dashtpagerdi M, Pourghasemi HR, Chen W. A Comparative Assessment Between Linear and Quadratic Discriminant Analyses (LDA-QDA) with Frequency Ratio and Weights-of-Evidence Models for Forest Fire Susceptibility Mapping in China. Arab J Geosci. 2017; 10(7): 1-14. [Crossref] [Google Scholar]
39. Kumari B, Pandey AC. Geo-Informatics Based Multi-Criteria Decision Analysis (MCDA) Through Analytic Hierarchyprocess (AHP) for Forest Fire Risk Mapping in Palamau Tiger Reserve, Jharkhand State, India. J Earth Syst Sci. 2020; 129(1): 1-16. [Crossref] [Google Scholar]
40. Eskandari S, Oladi Ghadikolaei J, Jalilvand H, Saradjian MR. Fire Risk Modeling and Prediction in District Three of Neka-Zalemroud Forest, Using Geographical Information System. Iran J For Pop Res. 2013; 21(2): 203-17. [Google Scholar]
41. Zarekar A, Kazemi Zamani B, Ghorbani S, Ashegh Moalla M, Jafari HR. Mapping Spatial Distribution of Forest Fire Using MCDM and GIS (Case Study: Three Forest Zones in Guilan Province). Iran J For Pop Res. 2013; 21(2): 218-30. [Google Scholar]
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