AHP and fuzzy logic geospatial approach for forest fire vulnerable zones

Abstract: Fires are devastating risky events in forests, having a negative effect on resources, biodiversity, economics, animal life, and putting people in danger. The goal of this study is to use geospatial techniques to identify areas in Jordan that are at risk of forest fires. The research area extends 50 kilometers north and 15 kilometers east from the Dead Sea. The forest fire risk zones map was developed using six factors: land cover class, aspect, proximity to settlements, elevation, slope, and proximity to roads. All of the factors have been selected based on their fire sensitivity or capacity to cause fire. In this study, a Turkish model with fuzzy logic and Analytical hierarchy analysis (AHP) was utilized to classify the area into five categories of risk ranging from very low to very high. According to the findings, approximately 12.12% of the study area is classified as very low risk, 25.54 % is classified as medium risk, while 12.84% is classified as very high risk. Over the last ten years, the map has been confirmed by prior fire occurrences using data from civil defense archives. This conclusion was very useful in gaining an understanding of the geographical distribution of fire-vulnerable zones. The research found that the GIS approach combined with AHP and fuzzy logic is a useful tool for estimating such kinds of maps.

How to cite this paper

 Shatnawi, N. (2022). AHP and fuzzy logic geospatial approach for forest fire vulnerable zones.Decision Science Letters , 11(4), 399-406.

Refrences

Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3), 1723-1743.‏
Ajin, R. S., Loghin, A. M., Vinod, P. G., & Jacob, M. K. (2017a). The risk analysis of potential forest fires in a wildlife sanctuary in the western ghats (Southwest Indian Peninsula) using geospatial techniques. International Journal of Health System and Disaster Management, 5(1), 18. 10.4103/ijhsdm.ijhsdm_26_16.
Ajin, R. S., Loghin, A. M., Vinod, P. G., & Jacob, M. K. (2017b). Mapping of forest fire risk zones in Peechi-Vazhani Wildlife Sanctuary, Thrissur, Kerala, India: a study using geospatial techniques. Journal of Wetlands Biodiversity, 7, 7-16.
Ajin, R. S., Loghin, A. M., Karki, A., Vinod, P. G., & Jacob, M. K. (2016). Delineation of forest fire risk zones in Thenmala forest division, Kollam, Kerala, India: A study using geospatial tools. Journal of Wetl Biodivers, 6, 139-48.
Anteur, D., Benaradj, A., Fekir, Y., & Baghdadi, D. (2021). Zakour Forest fire risk map assessment in the commune of Mamounia (Mascara, Algeria). Folia Forestalia Polonica, 63(1), 21-35. https://doi.org/10.2478/ffp-2021-0003
Ariapour, A., & Shariff, A. R. B. M. (2014). Rangeland fire risk zonation using remote sensing and geographical information system technologies in Boroujerd Rangelands, Lorestan Province, Iran. Ecopersia, 2(4), 805-818.
Coyle, R. G. (1989). A mission‐orientated approach to defense planning. Defense Analysis, 5(4), 353-367.
Dong, X. U., Li-min, D. A. I., Guo-fan, S., Lei, T., & Hui, W. (2005). Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of forestry research, 16(3), 169-174. doi:10.1007/bf02856809.
Erten, E., Kurgun, V., & Musaoglu, N. (2004, July). Forest fire risk zone mapping from satellite imagery and GIS: a case study. In XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey (pp. 222-230).
Eskandari, S. (2017). A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences, 10(8), 1-13.
Gheshlaghi, H. A. (2019). Using GIS to develop a model for forest fire risk mapping. Journal of the Indian Society of Remote Sensing, 47(7), 1173-1185.‏
Hasheminasab, S. A., Pirnazar, M., Hasheminasab, S. H. Z., Karimi, A., Eslamian, S. N., Ostad-Ali-Askari, K., ... & Mirkhalafi, A. (2017). Fire risk potential checking in forests using fire risk model. International Journal of Construction Resources and Civil Engineering, 3, 67-75. 10.20431/2454-8693.0304006.
Jamhawi, M. M., & Hajahjah, Z. A. (2017). A bottom-up approach for cultural tourism management in the old city of As-Salt, Jordan. Journal of Cultural Heritage Management and Sustainable Development, 7(1), 91-106. 10.1108/JCHMSD-07-2015-0027.
Kumari, B., & Pandey, A. C. (2020). Geo-informatics based multi-criteria decision analysis (MCDA) through analytic hierarchy process (AHP) for forest fire risk mapping in Palamau Tiger Reserve, Jharkhand state, India. Journal of Earth System Science, 129(1), 1-16. Doi: 10.1007/s12040-020-01461-6.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
Morales-Hidalgo, D., Oswalt, S. N., & Somanathan, E. (2015). Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecology and Management, 352, 68–77. doi:10.1016/j.foreco.2015.06.011
Bentekhici, N., Bellal, S. A., & Zegrar, A. (2020). Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria). Natural Hazards, 104(1), 811-831. 10.1007/s11069-020-04191-6.
PSD (2020) Archives of the general directorate of civil defense in Jordan. Available on: https://www.psd.gov.jo/
Saaty, T.L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
Youcef, F., Khalladi, M., Mohamed, L., Amine, H. M., & Djamel, A. (2020). Assessment of Forest Fire Risk and Forestry Species Degradation using GIS and Remote Sensing in Western of Algeria. Plant Archives, 20(2), 1793-1800.
Zadeh, L. A. (1975) The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8, 199–249

Journals

DSL Volumes

Keywords

Authors

Countries

Decision Science Letters

AHP and fuzzy logic geospatial approach for forest fire vulnerable zones Pages 399-406 Download PDF

Add Reviews