The Most Important Factors Influencing the Urmia Rangeland Fire Using DEMATEL

Souri, Mahshid; Najafi, Payam; Motamedi, Javad; Nateghi, Saeedeh

doi:10.22059/jrwm.2020.296573.1455

Document Type : Research Paper

Authors

¹ Assistant Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

² M.Sc. Graduate of Range Management, Faculty of Natural Resources, University of Urmia, Iran.

³ Professor associated, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

⁴ Assistant Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

https://doi.org/10.22059/jrwm.2020.296573.1455

Abstract

To determine these factors, the DEMATEL was used. To determine the most influential factors, several criteria such as slope, slope direction, height, type of cover, density of cover, percentage of cover, human population, proximity to roads, proximity to residential areas, proximity to agricultural lands, proximity to water resources, The type of employment of the natives and the use of the lands were used. The various steps of the decision evaluation method included forming the mean matrix, calculating the effect matrix of non-scaled direct relationships, calculating the total matrix (total direct and indirect effects matrix), calculating the impact matrix and the impact rate, and determining the order of effectiveness and impact. Based on the obtained results, among various factors, land use factor (3.9308) has the most impact and factor for slope has the least impact (1.0475) on the fire phenomenon. Based on the results of the present study, land use factors and human population have more interaction with other fire factors and the weight of these factors is more on the occurrence of fire phenomenon. Also, based on the results of the communication vector, which represents the certainty of a criterion as an influential criterion, the factors adjacent to the road (1.43) and height (0.6) have the greatest impact .The most important application of this information is the use of this information in the preparation of fire risk maps.

Keywords

20.1001.1.50442008.1399.73.4.8.4

References

[1] Akinola, O. V. and Adegoke, J. (2018). Assessment of forest fire vulnerability zones in Missouri, United States of America. International Journal of Sustainable Development & World Ecology, 1–7.

[2] Almedia, R. (1994). Forest fire risk areas and definition of the prevention priority planning actions using GIS. Proceedings of the Fifth European Conference and Exhibition on Geographic Information Systems, EGIS 94. Utrecht: EGIS Foundation, 2: 1700-1706.

[3] Amiri, T. (2019). Determining the location of observation towers in the rapid detection of forest fires using the geographic information system, Journal of Mapping and Spatial Information Engineering, 10(2):1-11.

[4] Burges, R. (2011). Development of a spatial, dynamic, fuzzy fire risk model for Chitwan District, Nepal. Geo-Information Science and Earth Observation for Enviromental Modeling and Management, 96 p.

[5] Chavan, M.E., Das, K.K. and Suryawanshi, R.S. (2012). Forest fire risk zonation using Remote Sensing and GIS in Huynial watershed, Tehri Garhwal district, UA. International Journal of Basic and Applied Research, 2: 6-12.

[6] Chuvieco, E., Aguado, I., Jurdao, S., Pettinari, M.L., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Ibarra, P., Rodrigues, M., Echeverria, M., Azqueta, D., Roman, M.V., Bastarrika, A., Martinez, S., Recondo, C., Zapico, E., and Martinez-Vega, F.J. (2012). Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23(5): 606-619.

[7] Çolak, E. and Sunar, F. (2020). Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction, 45, 471-479.

[8] Elmas, C. and Sonmez, Y. (2011). A data fusion framework with novel hybrid algorithm for multi-agent. Decision Support System for Forest Fire Cetin. Journal of Expert Systems with Applications, 38: 9225-9236.

[9] Erten, E., Kurgun, V. and Musaolu, N. (2005). Forest Fire Risk Zone Mapping From Satellite Imagery and GIS, a Case Study: Civil Engineering Faculty, Journal of Remote Sensing Division, 15(3): 1-7.

[10] Gandhi, S., Mangla, S.K., Kumar, P. and Kumar, D. (2015). Evaluation factors in implementation of successful green supply chain management using DEMATEL, A case study: Indian manufacturing company. International Journal of Strategic Management Review, 3(2): 96–109.

[11] Gerdzheva, A.A. (2014). A Comparative analysis of different wildfire risk assessment models (a case study for smolyyan distric, Bulgaria). European Journal of Geography, 5(3): 22-36.

[12] Han, Y. and Deng, Y. (2018). An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors. Journal of Soft Computing, 22(15), 5073–5090.

[13] Jaiswal, R.K., Mukherjee, S., Raju, K.D. and Saxena, R. (2002). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1): 1-10.

[14] Janbaz Ghobadi, GH., 2019. Investigation of forest fire risk areas in Golestan province based on fire risk index Using GIS, Iranian Journal of Space Analysis of Environmental Hazards, 6 (3):89-109.

[15] Ju, Y., Wang, A. and You, T. (2014). Emergency alternative evaluation and selection based on ANP, DEMATEL, and TL-TOPSIS. Journal of Natural Hazards, 75(52): 347–379.

[16] Kilic, H. and Yalcin, AS. (2020). Comparison of municipalities considering environmental sustainability via neutrosophic DEMATEL based TOPSIS. Socio-Economic Planning Sciences, Elsevier, 23, 117-128.

[17] Konkathi, P., Shetty, A., Kolluru, V., Yathish, P. and Pruthviraj, U. (2019). Static Fire Risk Index for the Forest Resources of Karnataka. International Geoscience and Remote Sensing Symposium, 115-117.

[18] Lozano, F.J., Suarez-Seoane, S., Kelly, M. and Luis, E. (2008). A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region, Journal of Remote Sensing of Environment, 112: 708-719.

[19] Martinez, J., Vega-Garcia, C. and Chuvieco, E. (2009). Human caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 90(2): 1241–1252.

[20] Mobarghai, N., Sherzei, GH.A. Makhdoum, M., Yavari, A.R. and Jafari, H.R. (2009). The Spatial Valuation Forests of Iran. Journal of Enviromental Science, 35(3): 57-68.

[21] Mohammad Yazdi, A., Nedjati, E. and Zarei, R. (2020). A novel extension of DEMATEL approach for probabilistic safety analysis in process systems. Safety Science journal, 121, 119-136.

[22] Naghipour, A. (2018). Forecasting the occurrence of fire using modeling of Bavar Lizin network in Chaharmahal and Bakhtiari province, Rangeland Scientific Research Journal, 13 (1): 90-100.

[23] Sakellariou, S., Tampekis, S., Samara, F., Flannigan, M., Jaeger, D., Christopoulou, O. and Sfougaris, A. 2018. Determination of fire risk to assist fire management for insular areas: the case of a small Greek island Journal of Forestry Research, 13,178-188.

[24] Sheng-Li, Si., Xiao-Yue, Y., Hu-Chen, L. and Zhang, P. (2018). DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications. Mathematical Problems in Engineering Journal, 41, 127-136.

[25] Thakur, A.K. and Singh, D. (2014). Forest Fire Risk Zonation Using Geospatial Techniques and Analytic Hierarchy Process in Dehradun District, Uttarakhand, India. Universal Journal of Environmental Research and Technology, 4(2): 82-89.

[26] Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Journal of Expert Systems with Applications, 36, 7738-7748.

[27] Tzeng, G.H. and Huang, J.J. (2011). Multiple Attribute Decision making methods and application, Taylor and Frncis Group.

[28] Vadrevu, K.P., Eaturu, A., Badarinath. K. (2010). Fire risk evaluation using multi criteria analysis– a case study. Journal of Environmental Monitoring and Assessment, 166(2): 223–239.

[29] Xue-qian, Sh., Moxian, S., Kai, H. and Wen, J. (2019). An improved evidential DEMATEL identify critical success factors under uncertain environment. Journal of Ambient Intelligence and Humanized Computing, 16 (3), 113-121. https://doi.org/10.1007/s12652-019-01546-1.

[30] Yang, J.L. and Tzeng, G.H. (2011). An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method. Journal of Expert system with applications, 38(2): 1417- 1424.

[31] Yiliadis, L.S. (2005). A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling and Software, 20(5): 613–621.

[32] Zarrakar, A., Kazemi Zamani, B., Ghorbani, S., Ashegh Maala, M. and Jafari, HR. (2013). Spatial Distribution Mapping of Forest Fire Risk Using Decision Making Method Multi Criteria and GIS in Three Forest Areas of Guilan Province. Iranian Journal of Forest and Poplar Research, 21 (2): 230- 218.

[33] Zhou, L., Dai, G., Qin, R., Tang, M. and Qiu, J. (2018). Risk Analysis of Gob Coal Spontaneous Combustion in Methane-Rich, Combustion-Prone Coal Seam Based on Intuitionistic Fuzzy DEMATEL. Journal of Failure Analysis and Prevention, 18(4), 975–987.

Journal of Range and Watershed Managment

The Most Important Factors Influencing the Urmia Rangeland Fire Using DEMATEL

References

References

Volume 73, Issue 4
March 2021
Pages 786-801

The Most Important Factors Influencing the Urmia Rangeland Fire Using DEMATEL

References

References

Volume 73, Issue 4March 2021Pages 786-801

Volume 73, Issue 4
March 2021
Pages 786-801