Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering
In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM) clustering. We used twelve different input parameters to model fire risk in Ilam Province. Our results with minimum support and minimum confidence show strong relationships between wildfire occurrence and eight variables (distance from settlement, population density, distance from road, slope, standing dead oak trees, temperature, land cover and distance from farm land). In this study, we defined three clusters for each variable: low, middle and high. The data regarding the factors affecting forest fire risk were distributed in these three clusters with different degrees of membership and the final map of all factors was classified by FCM clustering. Each layer was then created in a geographic information system. Finally, wildfire risks in the area obtained from overlaying these layers were classified into five categories, from very low to very high according to the degree of danger.
Adab Hamed, Kanniah Kasturi Devi, Solaimani Karim (2013): Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65, 1723-1743 https://doi.org/10.1007/s11069-012-0450-8
Aggarwal S., Rani B. (2013): Optimization of association rule mining process using Apriori and Ant Colony Optimization algorithm. International Journal of Current Engineering and Technology, 3: 620–623.
Agrawal R., Srikant R. (1994): Fast algorithms for mining association rules. In: Bocca J.B., Jarke M., Zaniolo C. (eds): Pro-ceedings of 20th International Conference on Very Large Databases, Santiago de Chile, Sept 12–15, 1994: 487–499.
Ahmadi R., Kiadaliri H., Mataji A., Kafaki S. (2014): Oak forest decline zonation using AHP model and GIS technique in Zagros Forests of Ilam Province. Journal of Biodiversity and Environmental Sciences (JBES), 4: 141–150.
Akbari D., Amini J., Saadatseresht M. (2008): Developing a simple and fast model to produce wildfire map for forest. In: 2nd Symposium on Facing Natural Hazard, Tehran, Dec 25–26, 2008: 1–7.
Arekhi Saleh, Jafarzadeh Ali Akbar (2014): Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests, Ilam province, Iran). Arabian Journal of Geosciences, 7, 1073-1085 https://doi.org/10.1007/s12517-012-0785-1
Bezdek J.C. (1981): Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, Kluwer Academic Publishers: 272.
Bowman D. M. J. S., Balch J. K., Artaxo P., Bond W. J., Carlson J. M., Cochrane M. A., D'Antonio C. M., DeFries R. S., Doyle J. C., Harrison S. P., Johnston F. H., Keeley J. E., Krawchuk M. A., Kull C. A., Marston J. B., Moritz M. A., Prentice I. C., Roos C. I., Scott A. C., Swetnam T. W., van der Werf G. R., Pyne S. J. (): Fire in the Earth System. Science, 324, 481-484 https://doi.org/10.1126/science.1163886
Cakir Ozgur, Aras Murat Efe (2012): A Recommendation Engine by Using Association Rules. Procedia - Social and Behavioral Sciences, 62, 452-456 https://doi.org/10.1016/j.sbspro.2012.09.074
Deng Hepu (1999): Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21, 215-231 https://doi.org/10.1016/S0888-613X(99)00025-0
Erensal Yasemin Claire, Öncan Temel, Demircan Murat Levent (2006): Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey. Information Sciences, 176, 2755-2770 https://doi.org/10.1016/j.ins.2005.11.004
Erten E., Kurgun V., Musaoğlu N. (2005): Forest Fire Risk Zone Mapping from Satellite Imagery and GIS: A Case Study. Fire Risk Report. Istanbul, Civil Engineering Faculty, Remote Sensing Division: 7.
Eskandari Saeedeh, Chuvieco Emilio (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., Ghadikolaei J.O., Jalilvand H., Saradjian M.R. (2013): Fire risk modeling and prediction in district three of Neka-Zalemroud forest using geographical information system. Iranian Journal of Forest and Poplar Research, 21: 203–217.
Ester M., Kriegel H.P., Sander J. (1997): Spatial data mining: A database approach. In: Scholl M., Voisard A. (eds): Advances in Spatial Databases. Proceedings of the 5th International Symposium on Spatial Databases, Berlin, July 15–18, 1997: 47–66.
Fattahi M. (2003): Zagros Oak Forests and the Most Important Degradation Factors. Tehran, Research Institute of Forests and Rangelands: 324.
Feng Chu (1995): Fuzzy multicriteria decision-making in distribution of factories: an application of approximate reasoning. Fuzzy Sets and Systems, 71, 197-205 https://doi.org/10.1016/0165-0114(94)00238-3
Forest, Range and Watershed Management Organization of Iran (2015): Annual Report on Forest Fires. Tehran, Forest & Rangelands Publications: 30.
Garavand S., Yaralli N., Sadeghi H. (2013): Spatial pattern and mapping fire risk occurrence at natural lands of Lorestan province. Iranian Journal of Forest and Poplar Research, 21: 231–242.
Gottwald Siegfried (2006): Universes of Fuzzy Sets and Axiomatizations of Fuzzy Set Theory. Part I: Model-Based and Axiomatic Approaches. Studia Logica, 82, 211-244 https://doi.org/10.1007/s11225-006-7197-8
Hu H., Jin S. (2002): Study on forest fire regime of Heilongjiang province II. Analysis on factors affecting fire dynamics and distributions. Scientia Silvae Sinicae, 38: 98–102.
Keane Robert E., Drury Stacy A., Karau Eva C., Hessburg Paul F., Reynolds Keith M. (2010): A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecological Modelling, 221, 2-18 https://doi.org/10.1016/j.ecolmodel.2008.10.022
Koutsias N., Martínez-Fernández J., Allgöwer B. (2010): Do factors causing wildfires vary in space? Evidence from geo-graphically weighted regression. GIScience & Remote Sensing, 47: 221–240.
Liu B. (2007): Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Berlin, Heidelberg, New York, Spring-er-Verlag: 532.
Loboda Tatiana V. (2009): Modeling fire danger in data-poor regions: a case study from the Russian Far East. International Journal of Wildland Fire, 18, 19- https://doi.org/10.1071/WF07094
Mahdavi A., Fallah Shamsi S., Nazari R. (2012): Forests and rangelands’ wildfire risk zoning using GIS and AHP techniques. Caspian Journal of Environmental Sciences, 10: 43–52.
Merril D.F., Alexander M.E. (eds) (1987): Glossary of Forest Fire Management Terms. 4th Ed. Ottawa, Canadian Committee on Forest Fire Management, National Research Council of Canada: 91.
Mikhailov L., Tsvetinov P. (2004): Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 5, 23-33 https://doi.org/10.1016/j.asoc.2004.04.001
Mirabolfathy M. (2013): Outbreak of charcoal disease on Quercus spp. and Zelkova carpinifolia trees in forests of Zagros and Alborz Mountains in Iran. Iranian Journal of Plant Pathology, 49: 257–263.
Mohammadi F., Shabanian N., Pourhashemi M., Fatehi P. (2011): Risk zone mapping of forest fire using GIS and AHP in a part of Paveh forests. Iranian Journal of Forest and Poplar Research, 18: 569–586. (in Persian)
Bravo David Nogués, Araújo Miguel B., Lasanta Teodoro, Moreno Juan Ignacio López (2008): Climate Change in Mediterranean Mountains during the 21st Century. AMBIO: A Journal of the Human Environment, 37, 280-285 https://doi.org/10.1579/0044-7447(2008)37[280:CCIMMD]2.0.CO;2
Padilla M., Vega-García C. (2013): On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. International Journal of Wildland Fire, 20, 46- https://doi.org/10.1071/WF09139
Parry Martin, Palutikof Jean, Hanson Clair, Lowe Jason (2008): Squaring up to reality. Nature Reports Climate Change, , 68-71 https://doi.org/10.1038/climate.2008.50
Patel B., Chaudhari V.K., Karan R.K., Rana Y.K. (2011): Optimization of association rule mining Apriori algorithm using ACO. International Journal of Soft Computing and Engineering, 1: 24–26.
Paz Shlomit, Carmel Yohay, Jahshan Faris, Shoshany Maxim (2011): Post-fire analysis of pre-fire mapping of fire-risk: A recent case study from Mt. Carmel (Israel). Forest Ecology and Management, 262, 1184-1188 https://doi.org/10.1016/j.foreco.2011.06.011
Prasad V. Krishna, Badarinath K.V.S., Eaturu Anuradha (2008): Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. Journal of Environmental Management, 86, 1-13 https://doi.org/10.1016/j.jenvman.2006.11.017
Jaiswal Rajeev Kumar, Mukherjee Saumitra, Raju Kumaran D., Saxena Rajesh (2002): Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4, 1-10 https://doi.org/10.1016/S0303-2434(02)00006-5
Rodrigues M., de la Riva J., Fotheringham S. (2014): Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Applied Geography, 48, 52-63 https://doi.org/10.1016/j.apgeog.2014.01.011
ROMERO-RUIZ M., ETTER A., SARMIENTO A., TANSEY K. (2010): Spatial and temporal variability of fires in relation to ecosystems, land tenure and rainfall in savannas of northern South America. Global Change Biology, 16, 2013-2023 https://doi.org/10.1111/j.1365-2486.2009.02081.x
Salamati H., Mostafalou H., Mastoori A., Honardoost F. (2011): Evaluation and provision of forest fire risk map using GIS in Golestan forests. In: Proceedings of the 1st International Conference on Wildfire in Natural Resources Lands, Gorgan, Oct 26–28, 2011: 56–65. (in Persian)
Shihab A.I., Burger P. (1998): The analysis of cardiac velocity MR images using fuzzy clustering. In: Proceedings of SPIE Medical Imaging. Physiology and Function from Multidimensional Images, San Diego, Feb 21, 1998: 176–183.
Srikant R., Agrawal R. (1996): Mining quantitative association rules in large relational tables. In: Widom J. (ed.): Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, June 4–6, 1996: 1–12.
Stolle F., Chomitz K.M., Lambin E.F., Tomich T.P. (2003): Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. Forest Ecology and Management, 179, 277-292 https://doi.org/10.1016/S0378-1127(02)00547-9
Taylor Stephen W., Alexander Martin E. (2006): Science, technology, and human factors in fire danger rating: the Canadian experience.. International Journal of Wildland Fire, 15, 121- https://doi.org/10.1071/WF05021
Tian X., Shu L., Zhao F., Wang M. (2012): Analysis of the conditions for lightning fire occurrence in Daxing’ anling region. Scientia Silvae Sinicae, 48: 98–103.
Wang Ying-Ming, Luo Ying, Hua Zhongsheng (2008): On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186, 735-747 https://doi.org/10.1016/j.ejor.2007.01.050
Dong Xu, Li-min Dai, Guo-fan Shao, Lei Tang, Hui Wang (2005): Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research, 16, 169-174 https://doi.org/10.1007/BF02856809
Zarekar A., Kazemi Zamani B., Ghorbani S., Ashegh Moalla M., Jafari H. (2013): Mapping spatial distribution of forest fire using MCDM and GIS (case study: Three forest zones in Guilan province). Iranian Journal of Forest and Poplar Research, 21: 218–230.