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Original Articles

A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping

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Pages 861-885 | Received 31 May 2014, Accepted 01 Nov 2014, Published online: 01 Dec 2014

References

  • Abba AH, Noor ZZ, Yusuf RO, Mohd Din MF, Abu Hassan MA. 2013. Assessing environmental impacts of municipal solid waste of Johor by analytical hierarchy process. Resour Conserv Recycl. 73:188–196.
  • Adab H, Devi Kanniah K, Solaimani K. 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards. 65:1723–1743.
  • Agarwal PK, Patil PK, Mehal R. 2013. A methodology for ranking road safety hazardous locations using analytical hierarchy process. Proc Soc Behav Sci. 104:1030–1037.
  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B. 2012. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci. 38:23–34.
  • Allard G. 2001. The fire situation in Islamic Republic of Iran. Global forest fire assessment 1990-2000. Working Paper 55, Rome. p. 198–202. http://www.fao.org/forestry/en/
  • Alvarez Grima M. 2000. Neuro-fuzzy modeling in engineering geology. Rotterdam: Balkema.
  • Alvarez Grima M, Babuska R. 1999. Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Mining Sci. 36:339–349.
  • Anderson HE. 1982. Aids to determining fuel models for estimating fire behavior. Intermountain Forest and Range Experiment Station General Technical Report INT-122. Ogden (UT): USDA Forest Service.
  • Angayarkkani K, Radhakrishnan N. 2011. An effective technique to detect forest fire region through ANFIS with spatial data. 3rd International Conference on Electronics Computer Technology (ICECT); 2011; Kanyakumari, India; p. 24–30. doi:10.1109/ICECTECH.2011.5941794
  • Antoninetti M, Binagli E, Rampini A, D’Angelo M. 1993. The integrated use of satellite and topographic data for forest fire hazard map. In: Winkler P, Balkema AA, editors. Remote sensing for monitoring the changing environment of Europe. Rotterdam: Brookfield; p. 179–184.
  • Artsybashev ES. 1983. Forest fires and their control. lst ed. New Delhi: Oxonian (in Russian, 1974).
  • Ayalew L, Yamagishi H, Marui H, Kanno T. 2005. Landslides in Sado Island of Japan. Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol. 81:432–445.
  • Babuska R. 1996. Fuzzy modelling and identification [dissertation]. Delft: Delft University of Technology.
  • Betanzos AA, Fontenla-Romero O, Guijarro-Berdinas B, Hernandez-Pereira E, Canda J, Jimenez E, Legido JL, Muniz S, Paz-Andrade C, Paz-Andrade MI. 2002. A neural network approach for forestal fire risk estimation. In: Van Harmelen F, editor. Proceedings of the 15th Eureopean Conference on Artificial Intelligence, ECAI'2002; 2002 July; Lyon, France. p. 643–647.
  • Bisquert M, Caselles E, Sanchez E, Caselles V. 2012. Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. J Wildland Fire. 1:1025–1029.
  • Bonham-Carter GF. 1994. Computer methods in the geosciences. Vol. 13. Ontario: Pergamon.
  • Carvalho JP, Carola M, Tome JAB. 2006. Forest fire modeling using rule-based fuzzy Cognitive maps and Voronoi based cellular automata. Annual meeting of the North American Fuzzy Information Processing Society. NAFIPS 2006; 2006 Jun 3–6; Quebec, Canada; p. 217–222.
  • Castro R, Chuvieco E. 1989. Modeling forest fire danger from geographic information systems. Geocarto Int. 13:15–23.
  • Cevik E, Topal T. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol. 44:949–962.
  • Chung CF, Fabbri AG. 2003. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 30:451–472.
  • Chuvieco E. 2003. Wildland fire danger estimation and mapping: the role of remote sensing data. Series in remote sensing. Vol. 4. Singapore: World Scientific.
  • Chuvieco E, Aguadoa I, Yebraa M. 2010. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model. 221:46–58.
  • Chuvieco E, Coceroa D, Riano D, Martinc P, Martıiez-Vega J, de la Riva J, Perez F. 2004. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ. 92:322–331.
  • Chuvieco E, Congalton RG. 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ. 29:147–159.
  • Cortez P, Morais A. 2007. A data mining approach to predict forest fires using meteorological data. In: Neves J, Santos MF, Machado J, editors. New Trends in Artificial Intelligence. Proceedings of the EPIA 2007 – Portuguese Conference on Artificial Intelligence; 2007 December; Guimarães, Portugal, p. 512–523.
  • DeBano LF, Neary DG, Ffolliott PF. 1998. Fire's effects on ecosystems. New York (NY): Wiley.
  • Dimopoulou M, Giannikos I. 2001. Spatial optimization of resources deployment for forest-fire management. Int Trans Oper Res. 8:523–534.
  • Dimopoulou M, Giannikos I. 2002. Towards an integrated framework for forest fire control. Eur J Oper Res. 152:476–486.
  • Egan JP. 1975. Signal detection theory and ROC analysis. Vol. 195. New York (NY): Academic Press; p. 266–268.
  • Ercanoglu M, Gokceoglu C. 2002. Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol. 41:720–730.
  • Erten E, Kurgun V, Musaoglu N. 2004. Forest fire risk zone mapping from satellite imagery and GIS: a case study. XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey; p. 222–230.
  • Esmali Ouri A, Amirian S. 2009. Landslide hazard zonation using MR and AHP methods and GIS techniques in Langan watershed, Ardabil, Iran. International Conference on ACRS 2009; Beijing, China.
  • Fabbri AG, Chung CF. 2001. Spatial support in landslide hazard prediction based on map overlays. Proceeding of International Association for Mathematical Geology Annual Meeting (IAMG 2001); 2001 Sep 10–12; Cancun, Mexico.
  • García M, Chuvieco E, Nieto H, Aguado I. 2008. Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sens Environ. 112:3618–3627.
  • Giri S, Nejadhashemi AP. 2014. Application of analytical hierarchy process for effective selection of agricultural best management practices. J Environ Manage. 132:165–177.
  • Gokceoglu C, Sonmez H, Zorlu K. 2009. Estimating the uniaxial compressive strength of some clay-bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models. Expert Syst. 26:176–190.
  • Hajeeh M, Al-Othman A. 2005. Application of the analytical hierarchy process in the selection of desalination plants. Desalination. 174:97–108.
  • Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A. 2006. Fire risk assessment using satellite data. Adv Space Res. 37:741–746. doi:10.1016/j.asr.2004.12.053.
  • Iwan S, Mahmud AR, Mansor S, Shariff ARM, Nuruddin AA. 2004. GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prev Manage 13(5):379–386.
  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest. Northern Iran. Int J Environ Sci Technol. 11:909–926.
  • Jager R. 1995. Fuzzy logic in control [dissertation]. Delft: Delft University of Technology.
  • Jaiswal RK, Mukherjee S, Raju KD, Saxena R. 2002. Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Observ Geoinform. 4:1–10.
  • Janbaz Ghobadi Gh, Gholizadeh B, Majidi Dashliburun O. 2012. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (case study, Golestan province). Int J Agric Crop Sci. 4:818–824.
  • Juang CH, Lee DH, Sheu C. 1992. Mapping slope failure potential using fuzzy sets. J Geotech Eng Div ASCE. 118:475–493.
  • Kayastha P, Dhital MR, De Smedt F. 2013. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci. 52:398–408.
  • Koetz B, Morsdorf F, van der Linden S, Curt T, Allgower B. 2008. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. For Ecol Manage. 256:263–271.
  • Komac M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in peri-alpine Slovenia. Geomorphology. 74:17–28.
  • Krasnow K, Schoennagel T, Veblen TT. 2009. Forest fuel mapping and evaluation of landfire fuel maps in Boulder County, Colorado, USA. For Ecol Manage. 257:1603–1612.
  • Kurtener D, Badenko V. 2000. Methodological framework based on fuzzy set theory for land use management. J Braz Comput Soc. 6:26–32.
  • Kushla JD, Ripple WJ. 1997. The role of terrain in a fire mosaic of a temperate coniferous forest. For Ecol Manage. 95:97–107.
  • Langenbrunner JR, Hemez FM, Booker JM, Ross TJ. 2010. Model choice considerations and information integration using analytical hierarchy process. Proc Soc Behav Sci. 2:7700–7701.
  • Leuenberger M, Kanevski M, Vega Orozco CD. 2013. Forest fires in a random forest. Eur Geosci Union Gen Assembly. 15:2013–3238.
  • Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GFB, Lima A. 2009. Forest fire risk mapping in the Brazilian Amazon using MODIS images and artificial neural networks. Int J Appl Earth Obs. 11:265–272.
  • Mamdani EH, Assilian S. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud. 7:1–13.
  • Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci. 61:221–236.
  • Nadeau LB, McRae DJ, Jin JZ. 2005. Development of a national fuel-type map for Canada using fuzzy logic : INFORMATION REPORT NOR-X-406. Edmonton (AB): Canadian Forest Service Northern Forestry Centre.
  • Nandi A, Shakoor A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol. 110:11–20.
  • Nefeslioglu HA, Gokceoglu C, Sonmez H. 2006. Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles. Eng Geol. 85:251–269.
  • Nefeslioglu HA, Sezer EA, Gokceoglu C, Ayas Z. 2013. A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput Geosci. 59:1–8.
  • Negnevitsky M. 2002. Artificial intelligence: a guide to intelligent systems. Harlow: Pearson Education; p. 394.
  • Noonan EK. 2003. A coupled model approach for assessing fire hazard at point Reyes national seashore: Flam Map and GIS. In: Second international wild land fire ecology and fire management congress and fifth symposium on fire and forest meteorology. Orlando (FL): American Meteorological Society; p. 127–128.
  • Osna T, Sezer EA, Akgun A. 2014. GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci. 66:20–30.
  • Pierce AD, Farris GA, Taylor AH. 2012. Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA. For Ecol Manage. 279:77–89.
  • Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122:349–369.
  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM. 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances, Nat Hazards. 69:749–779.
  • Pourghasemi HR, Pradhan B, Gokceoglu C. 2012a. Remote sensing data drived parameters and its use in landslide susceptibility assessment using Shannon's entropy and GIS. In: Varatharajoo R, Abdullah EJ, Majid DL, Romli FI, Mohd Rafie AS, Ahmad KA, editors. Applied Mechanics and Materials (Volume 225). AEROTECH IV, Chapter 7: Space Systems. p. 486–491.
  • Pourghasemi HR, Pradhan B, Gokceoglu C. 2012b. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. 63:965–996.
  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM. 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci. 7(5):1857–1878.
  • Pourtaghi ZS, Pourghasemi HR, Rossi M. 2014. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci. http://dx.doi.org/10.1007/s12665-014-3502-4
  • Pradhan B. 2010a. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci. 63(2): 329–349.
  • Pradhan B. 2010b. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Remote Sens. 38:301–320.
  • Pradhan B. 2011. Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat. 18:471–493.
  • Pradhan B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci. 51:350–365.
  • Pradhan B, Assilzadeh H. 2010. Forest fire detection and monitoring using high temporal MODIS and NOAA AVHRR satellite images in Peninsular Malaysia. Disaster Adv. 3:18–23.
  • Pradhan B, Lee S, Buchroithner MF. 2009. Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Appl Geomatics. 1:3–15.
  • Pradhan B, Suliman MDHB, Awang MAB. 2007. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prev Manage. 16:344–352.
  • Prasad VK, Badarinath KVS, Anuradha E. 2008. Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J Environ Manage. 86:1–13.
  • Prosper-Laget V, Douguedroitl A, Guinot JP. 1995. Mapping the risk of forest fire occurrence using NOAA satellite information. EAR seL Adv Remote Sens. 4:30–38.
  • Rautela P, Lakhera RC. 2000. Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). Int J Appl Earth Observ Geoinform. 2:153–160.
  • Rawat GS. 2003. Fire risk assessment for fire control management in Chilla forest range of Rajaji National Park Uttaranchal (India) [thesis]. Enschede: International Institute for Geo-information Science and Earth Observation.
  • Razali SBM. 2007. Forest fire hazard rating assessment in peat swamp forest using integrated remote sensing and geographical information system [thesis]. Malaysia:University Putra Malaysia.
  • Regmi AD, Yoshida K, Pourghasemi HR, Dhital MR, Pradhan B. 2014. Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mt Sci. 11(5):1266–1285.
  • Renard Q, Pelissier R, Ramesh BR, Kodandapani N. 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire. 21:368–379.
  • Rouse JW, Haas RH, Schell JA, Deering DW. 1973. Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In: Freden SC, Mercanti EP, Becker MA editors. Third earth resources technology satellite-1 Symposium- Volume I: Technical Presentations. NASA SP-351; Washington (DC): NASA. p. 309–317.
  • Saaty TL. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol. 15:234–281.
  • Saaty TL. 1994. Fundamentals of decision making and priority theory with analytic hierarchy process. Pittsburgh: RWS Publications; p. 527.
  • Saaty TL. 2000. Decision making for leaders: the analytical hierarchy process for decisions in a complex world. Pittsburgh: RWS Publications.
  • Saaty TL, Vargas LG. 2001. Models, methods, concepts and applications of the analytic hierarchy process. Dordrecht: Kluwer.
  • Saboya FJ, Alves MDG, Pinto WD. 2006. Assessment of failure susceptibility of soil slopes using fuzzy logic. Eng Geol. 86:211–224.
  • Safi Y, Bouroumi A. 2013. Prediction of forest fires using artificial neural networks. Appl Math Sci. 7:271–286.
  • Saklani P. 2008. Forest fire risk zonation, a case study Pauri Garhwal, Uttarakhand, India [dissertation]. Enschede: International Institute for Geo-information Science and Earth Observation.
  • Shadman Roodposhti M, Rahimi S, Jafar Beglou M. 2014. PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards. 73(1):77–95.
  • Stojanova D, Panov P, Kobler A, Dzeroski S, Taskova K. 2006. Learning to predict forest fires with different data mining techniques. Proceedings of the Conference on Data Mining and Data Warehouses; 2006 Oct 9; Ljubljana, Slovenia; p. 255–258.
  • Teodoro AC, Duarte L. 2013. Forest fire risk maps: a GIS open source application – a case study in Norwest of Portugal. Int J Geogr Inf Sci. 27:699–720.
  • Tierno NR, Puig AB, Vera JB, Verdu FM. 2013. The retail site location decision process using GIS and the analytical hierarchy process. Appl Geogr. 40:191–198.
  • van Westen C. 1997. Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. Enschede: ITC Publication; p. 73–84.
  • Vargas LG. 1990. An overview of the analytic hierarchy process and its applications. Eur J Oper Res. 48:2–8.
  • Weise DR, Biging GS. 1997. A qualitative comparison of fire spread models incorporating wind and slope effects. For Sci. 43:170–180.
  • Whelan RJ. 1995. The ecology of fire. Cambridge: Cambridge University Press.
  • Wu CH, Chen SC. 2009. Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology. 112:190–204.
  • Wulder MA, Franklin SE. 2006. Understanding forest disturbance and spatial pattern: remote sensing and GIS approaches. Boca Raton (FL): CRC Press (Taylor and Francis).
  • Xiangwei G, Xianyun F, Hongquan X. 2011. Forest fire risk zone evaluation based on high spatial resolution RS image in Liangyungang Huaguo Mountain Scenic Spot. IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM); 2011 Jun 29–2011 Jul 1; Fuzhou, China; p. 593–596.
  • Yager RR, Filev DP. 1994. Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern. 24:1279–1284.
  • Yagiz S, Gokceoglu C. 2010. Application of fuzzy inference and non-linear regression methods for predicting rock brittleness. Expert Syst Appl. 37:2265–2272.
  • Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena. 72:1–12.
  • Yesilnacar EK. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey [dissertation]. Melbourne: Department of Geomatics, The University of Melbourne; p. 423.
  • Youssef MA, Pradhan B, Tarabees E. 2011. Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arab J Geosci. 4:463–473.
  • Zadeh LA. 1965. Fuzzy sets. Inf Control. 8:338–352.
  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B. 2013. Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. 6 (8):2873–2888.
  • Zhang R, Zhang X, Yang J, Yuan H. 2013. Wetland ecosystem stability evaluation by using analytical hierarchy process (AHP) approach in Yinchuan Plain, China. Math Comput Model. 57:366–374.
  • Zhao J, Zhang Z, Han S, Qu C, Yuan Z, Zhang D. 2011. SVM based forest fire detection using static and dynamic features. Comput Sci Inf Syst. 8:821–841.

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