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Prioritization of forest fire hazard risk simulation using Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) coupled with multicriteria decision analysis (MCDA) techniques – a comparative study analysis

Abstract

Forests are important dynamic systems which are widely attracted by wild fires worldwide. Due to the complexity and non-linearity of the causative forest fire problems, employing sophisticated hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threats. This estimate will provide the outline of priority areas for preventing activities and allocation of fire fighters’ stations, seeking to minimize possible damages caused by fires. This study aims at prioritizing the forest fire risk of Wassa West district of Ghana. The study considered static causative factors such as Land use and land cover (which include forest, built-ups and settlement areas), slope, aspect, linear features (water bodies and roads) and dynamic causative factors such as wind speed, precipitation, and temperature were used. The methods employed include a Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) techniques. The fuzzy sets integrated with AHP in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. FAHP and HGRA methods were used for estimating the importance (weights) of the effective factors in forest fire modelling. Based on their modelling methods, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas were analyzed based on FAHP and HGRA. The major criteria models and fire risk model were presented based on these FAHP and HGRA weights. On the other hand, the spatial data of the sub criteria were provided and assembled in GIS environment to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risks of its classes to fire occurrence. The maps of each major criterion were obtained by weighted overlay of its sub criteria maps considering to major criterion model in GIS environment. Finally, the map of fire risk was obtained by weighted overlay of major criteria maps considering to fire risk model in GIS. The results showed that the FAHP model showed superiority than HGRA in prioritizing forest fire risk of the study area in terms of statistical analysis with a standard deviation of 0.09277 m as compared to 0.1122 m respectively. The obtained fire risk map can be used as a decision support system for predicting of the future trends in the study area. The optimized structures of the proposed models could serve as a good alternative to traditional forest predictive models, and this can be a promisingly testament used for future planning and decision making in the proposed areas.

Keyword : fire hazard risk modelling, fuzzy logics, grey relativity analysis, multicriteria decision analysis, soft computing techniques

How to Cite
Kumi-Boateng, B., Peprah, M. S., & Larbi, E. K. (2021). Prioritization of forest fire hazard risk simulation using Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) coupled with multicriteria decision analysis (MCDA) techniques – a comparative study analysis. Geodesy and Cartography, 47(3), 147-161. https://doi.org/10.3846/gac.2021.13028
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References

Addai, E. K., Tulashi, S. K., Annan, J. S., & Yeboah, I. (2016). Trend of fire outbreaks in Ghana and ways to prevent these incidents. Safety and Health at Work, 7(4), 284–292. https://doi.org/10.1016/j.shaw.2016.02.004

Akay, A. E., & Erdogan, A. (2017). GIS-Based multicriteria decision analysis for forest fire risk mapping. In ISPRS Annals of the Photo-grammetry, Remote Sensing and Spatial Information Sciences, Vol. IV-4/WA, 2017 4th International GeoAdvances Workshop (pp. 25–30), 14–15 October 2014, Safranboln, Karabuk, Turkey. https://doi.org/10.5194/isprs-annals-IV-4-W4-25-2017

Alonso-Betanzos, A., Fontenla-Romero, O., Guijarro-Berdiñas, B., Hernández-Pereira, E., Canda, E., Jimenez, J., Luis Legido, J., Muñiz, S., Paz-Andrade, C., & Paz-Andrade, M. I. (2002). A neural network approach for forest fire risk estimation. In F. van Harmelen (Ed.), Pro-ceedings of the 15th European Conference on Artificial Intelligence (pp. 643–647). ECAI Publisher, France.

Alonso-Betanzos, A., Fontenla-Romero, O., Guijarro-Berdiñas, B., Hernández-Pereira, E., Paz Andrade, M. I., Jiménez, E., Soto, J. L. L., & Carballas, T. (2003). An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Systems with Ap-plications, 25(4), 545–554. https://doi.org/10.1016/S0957-4174(03)00095-2

Amissah, I., Kyereh, B., & Agyemang, V. K. (2010). Wildfire incidence and management in the forest transition zone of Ghana: Farmers’ per-spectives. Ghana Journal of Forestry, 26, 61–73. https://doi.org/10.4314/gjf.v26i1.66202

Asante-Annor, A., Konadu, S. A., & Ansah, E. (2018). Determination of potential landfill site in Tarkwa area using multi-criteria GIS, geo-physical and geotechnical evaluation. Journal of Geoscience and Environment Protection, 6(10), 1–27. https://doi.org/10.4236/gep.2018.610001

Askin, O., & Guzin, O. (2007). Comparison of AHP and fuzzy AHP for the multi-criteria decision-making process with linguistic evaluations (Unpublished Technical Report). İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6, 65–85. https://dergipark.org.tr/en/download/article-file/199503

Asklund, R., & Eldvall, B. (2005). Contamination of water resources in Tarkwa mining area of Ghana [Unpublished MSc Thesis]. Depart-ment of Engineering Geology, Lund University. https://xdocs.net/documents/contamination-of-water-resources-in-tarkwa-mining-area-of-ghana-5ded56a614621

Atesoglu, A. (2014). Forest fire hazard identifying. Mapping using satellite imagery-geographic information system and analytic hierarchy process: Bartin-Turkey. Journal of Environmental Protection and Ecology, 15(2), 715–725. https://www.academia.edu/18013307/FOREST_FIRE_HAZARD_IDENTIFYING_MAPPING_USING_SATELLITE_IMAGERY_GEOGRAPHIC_INFORMATION_SYSTEM_AND_ANALYTIC_HIERARCHY_PROCESS_BARTIN_TURKEY

Avotri, T. S. M., Amegbey, N. A., Sandow, M. A., & Forson, S. A. K. (2002). The health impact of cyanide spillage at Goldfields Ghana limited (Unpublished Technical Report). Tarkwa, Ghana.

Boye, C. B., Peprah, M. S., & Kodie, N. K. (2018). Geographic assessment of telecommunication signals in a mining community: A case study of Tarkwa and its environs. Ghana Journal of Technology, 2(2), 41–49. https://www.researchgate.net/publication/324089809_Geographic_Assessment_of_Telecommunication_Signals_in_a_Mining_Community_A_Case_Study_of_Tarkwa_and_its_Environs

Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2

Chang, T. H., & Wang, T.C. (2009). Using the fuzzy multi-criteria decision-making approach for measuring the possibility of successful knowledge management. Information Sciences, 179(4), 355–370. https://doi.org/10.1016/j.ins.2008.10.012

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., & Martinez-Vega, F. J. (2014). Integrating geospa-tial information into fire risk assessment. International Journal of Wildland Fire, 23(5), 606–619. https://doi.org/10.1071/WF12052

Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147–159. https://doi.org/10.1016/0034-4257(89)90023-0

Cortez, P. G., & Morais, A. (2007). A data mining approach to predict forest fires using meteorological data. In Proceedings of the 13th Portu-guese Conference on Artificial Intelligence (pp. 512–523). Portugal. http://piano.dsi.uminho.pt/~pcortez/fires.pdf

Dong, X. U., Li-min, D., Guo-fan, S., Lei, T., & Hui, W. (2005). Forest fire risk zone mapping from satellite images and GIS for Baihe For-estry Bureau, Jilin, China. Journal of Forestry Research, 16(3), 169–174. https://doi.org/10.1007/BF02856809

Durmaz, B. D., Bilgili, E., Salam, B., Kucuk, O., Baysal, I., Kadiogullari, A. I., & Baskent, E. Z. (2008, July 2–5). Spatial forest fire risk anal-ysis and mapping using GIS. In 5th International Conference on Geographic Information Systems (ICGIS 2008) (pp. 115–120). Faith Uni-versity, Istanbul, Turkey. https://www.academia.edu/22246612/Proceedings_of_the_5th_International_Conference_on_Geographic_Information_Systems_-_2008_Vol._2

Erten, E., Kurgun, V., & Musaolu, N. (2005). Forest fire risk zone mapping from satellite imagery and GIS, a case study (Unpublished Pa-per). https://www.isprs.org/proceedings/XXXV/congress/yf/papers/927.pdf

Eskandari, S. (2015). Investigation on relationship between climate change and fire in the forests of Golestan forests. Iranian Journal of Forest and Range Protection Research, 13(1), 1–10. https://www.cabdirect.org/cabdirect/abstract/20173172299

Eskandari, S. (2017). A new approach for forest fire risk modelling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences, 10, 190. https://doi.org/10.1007/s12517-017-2976-2

Eskandari, S., & Chuvieco, E. (2015). Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42, 57–64. https://doi.org/10.1016/j.jag.2015.05.006

Eskandari, S., Oladi, J., Jalilvand, H., & Saradjian, M. R. (2013a). Role of human factors on fire occurrence in District Three of Neka Zalem-roud forests-Iran. World Applied Sciences Journal, 27(9), 1146–1150.

Eskandari, S., Oladi, J., Jalilvand, H., & Saradjian, M. R. (2013b). Detection of fire high-risk areas in Northern forests of Iran using Dong model. World Applied Sciences Journal, 27(6), 770–773. https://doi.org/10.5829/idosi.wasj.2013.27.06.503

Fazlollahtabar, H., Eslami, H., & Salmani, H. (2010). Designing a fuzzy expert system to evaluate alternatives in fuzzy analytic hierarchy pro-cess. Journal of Software Engineering and Applications, 3(4), 409–418. https://doi.org/10.4236/jsea.2010.34046

Fernandes, L. C., Cintra, R. S. C., Nero, M. A., & da Costa Temba, P. (2019). Fire risk modelling using artificial neural networks. In H. Rodrigues et al. (Eds.), EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization (pp. 938–948). Springer. https://doi.org/10.1007/978-3-319-97773-7_81

Firoozi, M. A., Goodarzi, M., & Shirali, R. (2017). Assessment and potential survey of lands in Khuzestan Province using the Buckley geo-metric mean model and Geographic Information System (GIS). Open Journal of Geology, 7(3), 234–241. https://doi.org/10.4236/ojg.2017.73016

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., & Wickham, J. (2011). Completion of the 2006 national land cover database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 77(9), 858–864. https://www.researchgate.net/publication/279868428_Completion_of_the_2006_National_Land_Cover_Database_for_the_Conterminous_United_States

Gai, C., Weng, W., & Yuan, H. (2011). GIS-based forest fire risk assessment and mapping. In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (pp. 1240–1244). IEEE. https://doi.org/10.1109/CSO.2011.140

Goldarag, Y. J., Mohammadzadeh, A., & Ardakani, A. S. (2016). Fire risk assessment using neural network and logistic regression. Journal of the Indian Society of Remote Sensing, 44, 885–894. https://doi.org/10.1007/s12524-016-0557-6

Joe-Asare, T., Peprah, M. S., & Opoku, M. M. (2018). Assessment of the potability of underground water from a small scale underground mine: A case study. Ghana Mining Journal, 18(2), 61–67. https://www.ajol.info/index.php/gm/article/view/181344

Jurdao, S., Chuvieco, E., & Arevalillo, J. M. (2012). Modelling fire ignition probability from satellite estimates of live fuel moisture content. Fire Ecology, 8(1), 77–97. https://doi.org/10.4996/fireecology.0801077

Kortatsi, B. K. (2004). Hydrochemistry of groundwater in the mining area of Tarkwa-Prestea, Ghana [Unpublished PhD Thesis]. University of Ghana, Legon-Accra, Ghana.

Larbi, E. K., Boye, C. B., & Peprah, M. S. (2018). A GIS approach in optimal route selection in the mining communities using the Analytical Hierarchy Process and the Least Cost Path analysis – A case study. In 5th UMaT Biennial International Mining and Mineral Conference (pp. 50–62). https://www.researchgate.net/publication/330780137_A_GIS_Approach_in_Optimal_Route_Selection_in_the_Mining_Communities_Using_the_Analytical_Hierarchy_Process_and_the_Least_Cost_Path_Analysis

Lozano, F. J., Suárez-Seoane, S., Kelly, M., & 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. Remote Sensing of Environment, 112(3), 708–719. https://doi.org/10.1016/j.rse.2007.06.006

Maeda, E. E., Formaggio, A. R., Shimabukuro, Y. E., Arcover­de, G. F. B., & Hansen, M. C. (2009). Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. International Journal of Applied Earth Observation and Geoinformation, 11(4), 265–272. https://doi.org/10.1016/j.jag.2009.03.003

Martinez, J., Vega-Garcia, C., & Chuvieco, E. (2009). Human-caused wildfire risk rating for prevention planning in Spain. Journal of Envi-ronmental Management, 90(2), 1241–1252. https://doi.org/10.1016/j.jenvman.2008.07.005

Perry, G. L. W. (1998). Current approaches to modelling the spread of wildland fire: A review. Progress in Physical Geography, 22(2), 222–245. https://doi.org/10.1177/030913339802200204

Peprah, M. S., Boye, C. B., Larbi, E. K., & Opoku Appau, P. (2018). Suitability analysis for sitting oil and gas filling stations using mul-ti-criteria decision analysis and GIS approach – A case study in Tarkwa and its environs. Journal of Geomatics, 12(2), 158–166.

Peprah, M. S., Ziggah, Y. Y., & Yakubu, I. (2017). Performance evaluation of the Earth Gravitational Model (EGM2008) – A case study. South African Journal of Geomatics, 6(1), 47–72. https://doi.org/10.4314/sajg.v6i1.4

Peprah, M. S., & Mensah, I. O. (2017). Performance evaluation of the Ordinary Least Square (OLS) and Total Least Square (TLS) in adjusting field data: An empirical study on a DGPS data. South African Journal of Geomatics, 6(1), 73–89. https://doi.org/10.4314/sajg.v6i1.5

Rollins, M. G., Keane, R. E., & Parsons, R. A. (2004). Mapping fuels and fire regimes using remote sensing, ecosystem simulation and gradi-ent modeling. Ecological Applications, 14(1), 75–95. https://doi.org/10.1890/02-5145

Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGrawHill. https://www.worldcat.org/title/analytic-hierarchy-process-planning-priority-setting-resource-allocation/oclc/5352839#div_lists_similar_items

Sakr, G. E., & Elhajj, I. H. (2010). Artificial intelligence for forest fire prediction: A comparative study. Proceedings of the Sixth International Conference on Forest Fire Research (pp. 653–661). Coimbra.

Satir, O., Berberoglu, S., & Donmez, C. (2016). Mapping regional forest fire probability using artificial neural network model in a Mediterra-nean forest ecosystem. Geomatics, Natural Hazards and Risk, 7(5), 1645–1658. https://doi.org/10.1080/19475705.2015.1084541

Sharma, L. K., Kanga, S., Nathawat, M. S., Sinha, S., & Pandey, P. C. (2012). Fuzzy AHP for forest fire risk modelling. Disaster Prevention and Management, 21(2), 160–171. https://doi.org/10.1108/09653561211219964

Sitanggang, I. S., Yaakob, R., Mustapha, N., & Ainuddin, A. N. (2013). Predictive models for hotspots occurrence using decision tree algo-rithm and logistic regression. Journal of Applied Sciences, 13(2), 252–261. https://doi.org/10.3923/jas.2013.252.261

Sowmya, S. V., & Somashekar, R. K. (2010). Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology, 31(6), 969–974. https://pubmed.ncbi.nlm.nih.gov/21506484/

Stolle, F., Chomitz, K. M., Lambin, E. F., & Tomich, T. P. (2003). Human ecological intervention and the role of forest fires in human ecology. Forest Ecology and Management, 179(1–3), 277–292. https://doi.org/10.1016/S0378-1127(02)00547-9

Stolzenburg, W. (2001). Fire in the rainforest. Nature Conservancy, 31, 22–27.

Suresh, B. K. V., Roy, A., & Prasad, P. R. (2016). Forest risk fire modelling in Uttarakhand Himalaya using TERRA satellite datasets. Euro-pean Journal of Remote Sensing, 49(1), 381–395. https://doi.org/10.5721/EuJRS20164921

Teodoro, A. C., & Duarte, L. (2013). Forest fire risk maps: a GIS open source application – a case study in Norwest of Portugal. International Journal of Geographical Information Science, 27(4), 699–720. https://doi.org/10.1080/13658816.2012.721554

Thapa, S., & Engelken, R., (2020). Optimization of pelleting parameters for producing composite pellets using agricultural and agro-processing wastes by Taguchi-Grey relational analysis. Carbon Resources Conversion, 3, 104–111. https://doi.org/10.1016/j.crcon.2020.05.001

Vadrevu, K. P., Eaturu, A., & Badarinath, K. V. S. (2010). Fire risk evaluation using multicriteria analysis – a case study. Environmental Mon-itoring and Assessment, 166(1–4), 223–239. https://doi.org/10.1007/s10661-009-0997-3

Vahidnia, M. H., Alesheikh, A., Alimohammadi, A., & Bassiri, A. (2008). Fuzzy analytical hierarchy process in GIS application. The Interna-tional Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B2), 593–396. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.155.2544&rep=rep1&type=pdf

Vakalis, D., Sarimveis, H., Kiranoudis, C. T., Alexandridis, A., & Bafas, G. V. (2004). A GIS based operational system for wildland fire crisis management. I. Mathematical modelling and simulation. Applied Mathematical Modelling, 28(4), 389–410. https://doi.org/10.1016/j.apm.2003.10.005

Vasconcelo, M. J., Silva, S., Tome, M., Alvim, M., & Pereira, J. M. C. (2001). Spatial prediction of fire ignition probabilities: Comparing logistic regression and neural networks. Photogrammetric Engineering & Remote Sensing, 67(1), 73–81. https://www.asprs.org/wp-content/uploads/pers/2001journal/january/2001_jan_73-81.pdf

Vasilakos, C., Kalabokidis, K., Hatzopoulos, J., & Matsinos, I. (2009). Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards, 50(1), 125–143. https://doi.org/10.1007/s11069-008-9326-3

Yakubu, I., Mireku-Gyimah, D., & Duker, A. A. (2015). Review of methods for modelling forest fire risk and hazard. African Journal of En-vironmental Science and Technology, 9(3), 155–165. https://doi.org/10.5897/AJEST2014.1820

Yakubu, I., Mireku-Gyimah, D., & Asafo-Adjei, D. (2018a). Hybrid methods for optimisation of weights in spatial multi-criteria evaluation decision for fire risk and hazard. World Academy of Science, Engineering and Technology, International Journal of Geotechnical and Geo-logical Engineering, International Scholarly and Scientific Research & Innovation, 12(12), 723–728. https://www.researchgate.net/publication/330193883_Hybrid-Methods-for-Optimisation-of-Weights-in-Spatial-Multi-Criteria-Evaluation-Decision-for-Fire-Risk-and-Hazard

Yakubu, I., Ziggah, Y. Y., & Peprah, M. S. (2018b). Adjustment of DGPS Data using artificial intelligence and classical least square tech-niques. Journal of Geomatics, 12(1), 13–20. https://isgindia.org/wp-content/uploads/2018/05/Pap_2_JoG_Vol_1_No2_April_2018.pdf

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–356. https://doi.org/10.1016/S0019-9958(65)90241-X

Zumbrunnen, T., Pezzatti, G. B., Menéndez, P., Bugmann, H., Bürgi, M., & Conedera, M. (2011). Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. Forest Ecology and Management, 261(12), 2188–2199. https://doi.org/10.1016/j.foreco.2010.10.009