GIS-based forest fire risk mapping using the analytical network process and fuzzy logic

This research investigates the efficiency of combining the Analytical Network Process (ANP) and fuzzy logic for developing a fire risk map. Major factors influencing the occurrence of forest fires were identified from the literature. The importance of each factor was determined by an ANP ranking procedure, which yielded the criteria weights, while fuzzy logic was employed for assessing the weights of the subcriteria. Then, GIS-based aggregation functions were employed to produce a fire risk map. In order to validate the results, forest fire locations were identified using field data, satellite images, and national reports. This validation revealed a very high accuracy of 0.819 for the fuzzy ANP model. The results will serve as guidelines for researchers and scientists by introducing new and robust MCDA methods. In general, the mentioned Hybrid method can be applied to early warning, fire suppression resources planning, and allocation work in the study area.

[1]  David L. Martell,et al.  A logistic model for predicting daily people-caused forest fire occurrence in Ontario , 1987 .

[2]  H. Pourghasemi.  GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models , 2016 .

[3]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[4]  Piermaria Corona,et al.  VALUTAZIONE DELLE RISORSE FORESTALI A LIVELLO GLOBALE , 2013 .

[5]  Nikola Kadoić,et al.  Decision making with the analytic network process , 2017 .

[6]  Zohre Sadat Pourtaghi,et al.  Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.

[7]  S. Eskandari,et al.  Detection of Fire High-Risk Areas in Northern Forests of Iran Using Dong Model , 2013 .

[8]  Yosio Edemir Shimabukuro,et al.  Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[9]  A. Mohammadzadeh,et al.  Fire Risk Assessment Using Neural Network and Logistic Regression , 2016, Journal of the Indian Society of Remote Sensing.

[10]  Wei-Wen Wu,et al.  Developing global managers' competencies using the fuzzy DEMATEL method , 2007, Expert Syst. Appl..

[11]  Rodrigo Prante Dill,et al.  Organization's Profitability Analysis: A Fuzzy Logic Approach , 2005 .

[12]  Gwo-Hshiung Tzeng,et al.  An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method , 2011, Expert Syst. Appl..

[13]  A. Althouse.  Statistical graphics in action: making better sense of the ROC curve. , 2016, International journal of cardiology.

[14]  K. Sagheb‐Talebi,et al.  The impact of fire on the forest and plants diversity in Iranian Oak forest , 2013, International Journal of Advanced Biological and Biomedical Research.

[15]  K. Solaimani,et al.  Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques , 2012, Natural Hazards.

[16]  B. Pradhan,et al.  Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran , 2012 .

[17]  Netra R. Regmi,et al.  Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA , 2010 .

[18]  Martine De Cock,et al.  An evolved fuzzy logic system for fire size prediction , 2009, NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society.

[19]  A. Kokhanovsky,et al.  Synergetic cloud fraction determination for SCIAMACHY using MERIS , 2010 .

[20]  Wenhui Wang,et al.  Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests , 2016 .

[21]  Mohammad Javad Valadan Zoje,et al.  Spatial analysis of fire potential in Iran using RS and GIS , 2010 .

[22]  Steffen Fritz,et al.  A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics , 2008 .

[23]  Peter Hofmann,et al.  A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis , 2017 .

[24]  F. Samadzadegan,et al.  A COMPARATIVE STUDY OF THREE ALGORITHMS FOR FOREST FIRE DETECTION IN IRAN , 2008 .

[25]  X. Lee,et al.  Introduction to wildland fire , 1997 .

[26]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[27]  Lazaros S. Iliadis,et al.  A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation , 2005, Environ. Model. Softw..

[28]  I. Yakubu,et al.  Review of methods for modelling forest fire risk and hazard , 2015 .

[29]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[30]  J. M. V. Samani,et al.  A New Integrated MADM Technique Combined with ANP, FTOPSIS and Fuzzy Max-Min Set Method for Evaluating Water Transfer Projects , 2014, Water Resources Management.

[31]  R. B. Jackson,et al.  The Structure, Distribution, and Biomass of the World's Forests , 2013 .

[32]  Zohre Sadat Pourtaghi,et al.  Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .

[33]  Thomas L. Saaty,et al.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[34]  Metin Dagdeviren,et al.  Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm , 2007, Inf. Sci..

[35]  Dieu Tien Bui,et al.  Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..

[36]  Hamid Reza Pourghasemi,et al.  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 , 2016 .

[37]  Weiguo Song,et al.  Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk , 2009 .

[38]  Feng-jun Zhao,et al.  Distribution characteristics and the influence factors of forest fires in China , 2013 .

[39]  A. Scott.  The Pre-Quaternary history of fire , 2000 .

[40]  Alan Grainger,et al.  Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015 , 2015 .

[41]  Prem Chandra Pandey,et al.  Fuzzy AHP for forest fire risk modeling , 2012 .

[42]  Nilton Cesar Fiedler,et al.  Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. , 2016, Journal of environmental management.

[43]  M. Kimothi,et al.  Application of Geographic Information System in Identification of 'Fire-Prone' Areas - a Feasibility Study in Parts of Junagadh (Gujarat) , 1998 .

[44]  M. Soto.  The identification and assessment of areas at risk of forest fire using fuzzy methodology , 2012 .

[45]  Bakhtiar Feizizadeh,et al.  Fuzzy Analytical Hierarchical Process and Spatially Explicit Uncertainty Analysis Approach for Multiple Forest Fire Risk Mapping , 2015 .

[46]  Biswajeet Pradhan,et al.  A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area , 2017 .

[47]  Moslem Akbarinia,et al.  Forest fire effects in beech dominated mountain forest of Iran , 2010 .

[48]  A. Nardini,et al.  Effects of prescribed burning on ecophysiological, anatomical and stem hydraulic properties in Pinus pinea L. , 2016, Tree physiology.

[49]  Shih-Jieh Hung,et al.  Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach , 2011, Expert Syst. Appl..

[50]  Abdullah E. Akay,et al.  Gis-Based Multi-Criteria Decision Analysis for Forest Fire Risk Mapping , 2017 .

[51]  PradhanBiswajeet,et al.  Landslide susceptibility assessment and factor effect analysis , 2010 .

[52]  Timothy C. Coburn,et al.  GIS and Multicriteria Decision Analysis , 2000 .

[53]  Jinghu Pan,et al.  Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China , 2016, Natural Hazards.

[54]  Rajeev Kumar,et al.  Receiver operating characteristic (ROC) curve for medical researchers , 2011, Indian pediatrics.

[55]  D G Altman,et al.  Statistics Notes: Diagnostic tests 3: receiver operating characteristic plots , 1994, BMJ.

[56]  R. Tomlinson,et al.  Uncertainty analysis in the application of multi-criteria decision-making methods in Australian strategic environmental decisions , 2013 .

[57]  Sachin Kumar Chhetri,et al.  Manifestation of an Analytic Hierarchy Process (AHP) Model on Fire Potential Zonation Mapping in Kathmandu Metropolitan City, Nepal , 2015, ISPRS Int. J. Geo Inf..

[58]  Luis G. Vargas,et al.  Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks , 2013 .

[59]  T. Saaty,et al.  Dependence and independence: From linear hierarchies to nonlinear networks , 1986 .

[60]  Hamid Reza Pourghasemi,et al.  A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China , 2017, Arabian Journal of Geosciences.

[61]  Mongkut Piantanakulchai,et al.  Analytic network process model for landslide hazard zonation , 2006 .

[62]  Youssef Safi,et al.  Prediction of forest fires using Artificial neural networks , 2013 .

[63]  Mahmood Taheri.  آشنایی با مجموعه های فازی , 2006 .

[64]  Bruna E. Z. Leal,et al.  Onboard fuzzy logic approach to active fire detection in Brazilian amazon forest , 2016, IEEE Transactions on Aerospace and Electronic Systems.