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Risk of fire occurrence in arid and semi-arid ecosystems of Iran: an investigation using Bayesian belief networks

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Abstract

Identifying areas that have a high risk of burning is a main component of fire management planning. Although the available tools can predict the fire risks, these are poor in accommodating uncertainties in their predictions. In this study, we accommodated uncertainty in wildfire prediction using Bayesian belief networks (BBNs). An influence diagram was developed to identify the factors influencing wildfire in arid and semi-arid areas of Iran, and it was populated with probabilities to produce a BBNs model. The behavior of the model was tested using scenario and sensitivity analysis. Land cover/use, mean annual rainfall, mean annual temperature, elevation, and livestock density were recognized as the main variables determining wildfire occurrence. The produced model had good accuracy as its ROC area under the curve was 0.986. The model could be applied in both predictive and diagnostic analysis for answering “what if” and “how” questions. The probabilistic relationships within the model can be updated over time using observation and monitoring data. The wildfire BBN model may be updated as new knowledge emerges; hence, it can be used to support the process of adaptive management.

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References

  • Aalders, I., Hough, R., & Towers, W. (2011). Risk of erosion in peat soils—an investigation using Bayesian belief networks. Soil Use and Management, 27(4), 538–549. doi:10.1111/j.1475-2743.2011.00359.x.

    Article  Google Scholar 

  • 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. doi:10.1007/s11069-012-0450-8.

    Article  Google Scholar 

  • Alencar, A. A., Solórzano, L. A., & Nepstad, D. C. (2004). Modeling forest understory fires in an eastern Amazonian landscape. Ecological Applications, 14(4), 139–149. doi:10.1890/01-6029.

    Article  Google Scholar 

  • Amatulli, G., Rodrigues, M. J., Trombetti, M., & Lovreglio, R. (2006). Assessing long-term fire risk at local scale by means of decision tree technique. Journal of Geophysical Research, 111(G04S05), 1–15. doi:10.1029/2005JG000133.

    Google Scholar 

  • Bashari, H., & Hemami, M.-R. (2013). A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran. Journal for Nature Conservation, 21(5), 319–325. doi:10.1016/j.jnc.2013.03.005.

    Article  Google Scholar 

  • Bashari, H., Smith, C., & Bosch, O. J. H. (2009). Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks. Agricultural Systems, 99(1), 23–34. doi:10.1016/j.agsy.2008.09.003.

    Article  Google Scholar 

  • Bond, W. J., & Keeley, J. E. (2005). Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends in Ecology & Evolution, 20(7), 387–394. doi:10.1016/j.tree.2005.04.025.

    Article  Google Scholar 

  • Borsuk, M. E., Stow, C. A., & Reckhow, K. H. (2003). Integrated approach to total maximum daily load development for Neuse River Estuary using Bayesian probability network model (Neu-BERN). Journal of Water Resources Planning and Management, 129(4), 271–282. doi:10.1061/(ASCE)0733-9496(2003)129:4(271).

    Article  Google Scholar 

  • Bromley, J., Jackson, N. A., Clymer, O., Giacomello, A. M., & Jensen, F. V. (2005). The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling & Software, 20(2), 231–242. doi:10.1016/j.envsoft.2003.12.021.

    Article  Google Scholar 

  • Brooks, M. L., & Matchett, J. R. (2006). Spatial and temporal patterns of wildfires in the Mojave Desert, 1980–2004. Journal of Arid Environments, 67, 148–164. doi:10.1016/j.jaridenv.2006.09.027.

    Article  Google Scholar 

  • Cain, J. (2001). Planning improvements in natural resources management: guidelines for using Bayesian networks to support the planning management of development programmes in the water sector and beyond. Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, UK.

  • Cardille, J. A., & Ventura, S. J. (2001). Occurrence of wildfire in the northern Great Lakes Region: effects of land cover and land ownership assessed at multiple scales. International Journal of Wildland Fire, 10(2), 145–154. doi:10.1071/WF01010.

    Article  Google Scholar 

  • Cardille, J. A., Ventura, S. J., & Turner, M. G. (2001). Environmental and social factors influencing wildfires in the Upper Midwest, United States. Ecological Applications, 11(1), 111–127. doi:10.1890/1051-0761(2001)011[0111:EASFIW]2.0.CO;2.

    Article  Google Scholar 

  • Catry, F. X., Rego, F. C., Bação, F., & Moreira, F. (2009). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921–931. doi:10.1071/WF07123.

    Article  Google Scholar 

  • Chas-Amil, M. L., Prestemon, J. P., McClean, C. J., & Touza, J. (2015). Human-ignited wildfire patterns and responses to policy shifts. Applied Geography, 56, 164–176. doi:10.1016/j.apgeog.2014.11.025.

    Article  Google Scholar 

  • De Santa Olalla, F. M., Dominguez, A., Ortega, F., Artigao, A., & Fabeiro, C. (2007). Bayesian networks in planning a large aquifer in Eastern Mancha, Spain. Environmental Modelling & Software, 22(8), 1089–1100. doi:10.1016/j.envsoft.2006.05.020.

    Article  Google Scholar 

  • Dilts, T. E., Sibold, J. S., & Biondi, F. (2009). A weights-of-evidence model for mapping the probability of fire occurrence in Lincoln County, Nevada. Annals of the Association of American Geographers, 99(4), 712–727. doi:10.1080/00045600903066540.

    Article  Google Scholar 

  • Dlamini, W. M. (2010). A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland. Environmental Modelling & Software, 25(2), 199–208. doi:10.1016/j.envsoft.2009.08.002.

    Article  Google Scholar 

  • Dlamini, W. M. (2011). Application of Bayesian networks for fire risk mapping using GIS and remote sensing data. GeoJournal, 76(3), 283–296. doi:10.1007/s10708-010-9362-x.

    Article  Google Scholar 

  • Dong, X., Li-min, D., 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.

    Article  Google Scholar 

  • Donoghue, L. R., & Main, W. A. (1985). Some factors influencing wildfire occurrence and measurement of fire prevention effectiveness. Journal of Environmental Management, 20(1), 87–96.

    Google Scholar 

  • Dorner, S., Shi, J., & Swayne, D. (2007). Multi-objective modelling and decision support using a Bayesian network approximation to a non-point source pollution model. Environmental Modelling & Software, 22(2), 211–222. doi:10.1016/j.envsoft.2005.07.020.

    Article  Google Scholar 

  • Duncan, B. W., Adrian, F. W., & Stolen, E. D. (2010). Isolating the lightning ignition regime from a contemporary background fire regime in east-central Florida, USA. Canadian Journal of Forest Research, 40(2), 286–297. doi:10.1139/X09-193.

    Article  Google Scholar 

  • Dwyer, E., Grégoire, J.-M., & Pereira, J. M. C. (2000). Climate and vegetation as driving factors in global fire activity. In J. L. Innes, M. Beniston, & M. M. Verstraete (Eds.), Biomass burning and its inter-relationships with the climate system (pp. 171–191). New York: Springer.

    Chapter  Google Scholar 

  • Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49.

    Article  Google Scholar 

  • Fry, D. L., & Stephens, S. L. (2006). Influence of humans and climate on the fire history of a ponderosa pine-mixed conifer forest in the southeastern Klamath Mountains, California. Forest Ecology and Management, 223(1), 428–438. doi:10.1016/j.foreco.2005.12.021.

    Article  Google Scholar 

  • Fuentes-Santos, I., Marey-Pérez, M. F., & González-Manteiga, W. (2013). Forest fire spatial pattern analysis in Galicia (NW Spain). Journal of Environmental Management, 128, 30–42. doi:10.1016/j.jenvman.2013.04.020.

    Article  CAS  Google Scholar 

  • Fuls, E. (1992). Ecosystem modification created by patch-overgrazing in semi-arid grassland. Journal of Arid Environments, 23(1), 59–69.

    Google Scholar 

  • Guevara, J. C., Stasi, C. R., Wuilloud, C. F., & Estevez, O. R. (1999). Effects of fire on rangeland vegetation in south-western Mendoza plains (Argentina): composition, frequency, biomass, productivity and carrying capacity. Journal of Arid Environments, 41(1), 27–35. doi:10.1006/jare.1998.0463.

    Article  Google Scholar 

  • Haubensak, K., D'antonio, C., & Wixon, D. (2009). Effects of fire and environmental variables on plant structure and composition in grazed salt desert shrublands of the Great Basin (USA). Journal of Arid Environments, 73(6), 643–650. doi:10.1016/j.jaridenv.2008.12.020.

    Article  Google Scholar 

  • Henriksen, H. J., Rasmussen, P., Brandt, G., Von Bülow, D., & Jensen, F. V. (2007). Public participation modelling using Bayesian networks in management of groundwater contamination. Environmental Modelling & Software, 22(8), 1101–1113. doi:10.1016/j.envsoft.2006.01.008.

    Article  Google Scholar 

  • Hessl, A., Miller, J., Kernan, J., Keenum, D., & McKenzie, D. (2007). Mapping paleo-fire boundaries from binary point data: comparing interpolation methods. The Professional Geographer, 59(1), 87–104. doi:10.1111/j.1467-9272.2007.00593.x.

    Article  Google Scholar 

  • Jensen, F. V. (1996). An introduction to Bayesian networks. New York: Springer.

    Google Scholar 

  • Jensen, F. V. (2001). Bayesian networks and decision graphs. New York: Springer.

    Book  Google Scholar 

  • Kalabokidis, K. D., Koutsias, N., Konstantinidis, P., & Vasilakos, C. (2007). Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area, 39(3), 392–402. doi:10.1111/j.1475-4762.2007.00756.x.

    Article  Google Scholar 

  • Kaloudis, S., Tocatlidou, A., Lorentzos, N. A., Sideridis, A. B., & Karteris, M. (2005). Assessing wildfire destruction danger: a decision support system incorporating uncertainty. Ecological Modelling, 181(1), 25–38. doi:10.1016/j.ecolmodel.2004.06.021.

    Article  Google Scholar 

  • Keane, R. E., Garner, J. L., Schmidt, K. M., Long, D. G., Menakis, J. P., & Finney, M. A. (1998). Development of input data layers for the FARSITE fire growth model for the Selway-Bitterroot Wilderness Complex, USA. RMRS GTR-3. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, USA.

  • Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence, 2 edition (computer science and data analysis. Chapman & Hall/CRC, Boca Raton). London: CRC Press.

  • Kunkel, K. E. (2001). Surface energy budget and fuel moisture. In E. A. Johansson & K. Miyanishi (Eds.), Forest fires: behavior and ecological effects (pp. 303–350). San Diego: Academic.

    Chapter  Google Scholar 

  • Lavorel, S., Flannigan, M. D., Lambin, E. F., & Scholes, M. C. (2007). Vulnerability of land systems to fire: interactions among humans, climate, the atmosphere, and ecosystems. Mitigation and Adaptation Strategies for Global Change, 12(1), 33–53. doi:10.1007/s11027-006-9046-5.

    Article  Google Scholar 

  • Legge, T. (1996). The beginning of caprine domestication in Southwest Asia. In D. R. Harris (Ed.), The origins and spread of agriculture and pastoralism in Eurasia (pp. 238–262). London: UCL Press.

    Google Scholar 

  • Leone, V., Lovreglio, R., Martín, M. P., Martínez, J., & Vilar, L. (2009). Human factors of fire occurrence in the Mediterranean. In C. Emilio (Ed.), Earth observation of wildland fires in Mediterranean ecosystems (pp. 149–170). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  • 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. doi:10.1016/j.rse.2007.06.006.

    Article  Google Scholar 

  • Maingi, J. K., & Henry, M. C. (2007). Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire, 16(1), 23–33. doi:10.1071/WF06007.

    Article  Google Scholar 

  • Manaswini, G., & Sudhakar Reddy, C. (2015). Geospatial monitoring and prioritization of forest fire incidences in Andhra Pradesh, India. Environmental Monitoring and Assessment, 187(10), 1–12. doi:10.1007/s10661-015-4821-y.

    Article  Google Scholar 

  • Manel, S., Williams, H. C., & Ormerod, S. J. (2001). Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38(5), 921–931. doi:10.1046/j.1365-2664.2001.00647.x.

    Article  Google Scholar 

  • Marcot, B. G., Holthausen, R. S., Raphael, M. G., Rowland, M. M., & Wisdom, M. J. (2001). Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management, 153(1–3), 29–42. doi:10.1016/S0378-1127(01)00452-2.

    Article  Google Scholar 

  • Marcot, B. G., Steventon, J. D., Sutherland, G. D., & McCann, R. K. (2006). Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research, 36(12), 3063–3074. doi:10.1139/X06-135.

    Article  Google Scholar 

  • Molina, J., Bromley, J., García-Aróstegui, J., Sullivan, C., & Benavente, J. (2010). Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks. Environmental Modelling & Software, 25(4), 383–397. doi:10.1016/j.envsoft.2009.10.007.

    Article  Google Scholar 

  • Mouillot, F., Ratte, J.-P., Joffre, R., Moreno, J. M., & Rambal, S. (2003). Some determinants of the spatio-temporal fire cycle in a Mediterranean landscape (Corsica, France). Landscape Ecology, 18(7), 665–674. doi:10.1023/B:LAND.0000004182.22525.a9.

    Article  Google Scholar 

  • Naghipour, A. A., Khajeddin, S. J., Bashari, H., Iravani, M., & Tahmasebi, P. (2015). The effects of fire on density, diversity and richness of soil seed bank in semi-arid rangelands of central Zagros region, Iran. Journal of Biodiversity and Environmental Sciences, 6(5), 311–318.

    Google Scholar 

  • Nielsen, T. D., & Jensen, F. V. (2009). Bayesian networks and decision graphs. New York: Springer.

    Google Scholar 

  • Norsys Software Corporation (2014). Netica TM Application for belief networks and influence diagrams: User’s Guide. Norsys Software Corporation, Vancouver, Canada.

  • Ofren, R. S., & Harvey, E. (2000). A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence, integrating tools proceeding. In M. Hansen & T. Burk (Eds.), Integrated tools for natural resources inventories in the twenty-first century (pp. 221–227). USA: Idaho.

    Google Scholar 

  • Papakosta, P., & Straub, D. (2011). Effect of weather conditions, geography and population density on wildfire occurrence: a Bayesian network model. In M. H. Faber, J. Köhler, & K. Nishijim (Eds.), Applications of statistics and probability in civil engineering. Zürich: CRC Press.

    Google Scholar 

  • Pausas, J. G. (2004). Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Climatic Change, 63(3), 337–350. doi:10.1023/B:CLIM.0000018508.94901.9c.

    Article  Google Scholar 

  • Pearl, J., & Russell, S. (2000). Bayesian networks. In M. Arbib (Ed.), The handbook of broain theory and neural networks. USA: MIT Press.

    Google Scholar 

  • Pepe, M. S., Cai, T., & Longton, G. (2006). Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics, 62(1), 221–229. doi:10.1111/j.1541-0420.2005.00420.x.

    Article  Google Scholar 

  • Pielke, R. A., & Conant, R. T. (2003). Best practices in prediction for decision-making: lessons from the atmospheric and earth sciences. Ecology, 84(6), 1351–1358. doi:10.1890/0012-9658(2003)084[1351:BPIPFD]2.0.CO;2.

    Article  Google Scholar 

  • Plucinksi, M. (2011). A review of wildfire occurrence research. Australia: Bushfire Cooperative Research Centre.

    Google Scholar 

  • Pollino, C. A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B. T. (2007). Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software, 22(8), 1140–1152. doi:10.1016/j.envsoft.2006.03.006.

    Article  Google Scholar 

  • Renkin, R. A., & Despain, D. G. (1992). Fuel moisture, forest type, and lightning-caused fire in Yellowstone National Park. Canadian Journal of Forest Research, 22(1), 37–45. doi:10.1139/x92-005.

    Article  Google Scholar 

  • Riaño, D., Moreno Ruiz, J. A., Barón Martínez, J., & Ustin, S. L. (2007). Burned area forecasting using past burned area records and Southern Oscillation Index for tropical Africa (1981–1999). Remote Sensing of Environment, 107(4), 571–581. doi:10.1016/j.rse.2006.10.008.

    Article  Google Scholar 

  • Romero-Calcerrada, R., Novillo, C. J., Millington, J. D. A., & Gomez-Jimenez, I. (2008). GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landscape Ecology, 23(3), 341–354. doi:10.1007/s10980-008-9190-2.

    Article  Google Scholar 

  • Sahu, S. K., & Mardia, K. V. (2005). A Bayesian kriged Kalman model for short-term forecasting of air pollution levels. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(1), 223–244. doi:10.1111/j.1467-9876.2005.00480.x.

    Article  Google Scholar 

  • Scholes, R. (1990). The influence of soil fertility on the ecology of southern African dry savannas. Journal of Biogeography, 17, 415–419. doi:10.2307/2845371.

    Article  Google Scholar 

  • Syphard, A. D., Radeloff, V. C., Keuler, N. S., Taylor, R. S., Hawbaker, T. J., Stewart, S. I., et al. (2008). Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire, 17(5), 602–613. doi:10.1071/WF07087.

    Article  Google Scholar 

  • Tansey, K., Grégoire, J. M., Stroppiana, D., Sousa, A., Silva, J., Pereira, J., et al. (2004). Vegetation burning in the year 2000: global burned area estimates from SPOT VEGETATION data. Journal of Geophysical Research, 109, D14S03. doi:10.1029/2003JD003598.

    Article  Google Scholar 

  • Ticehurst, J. L., Newham, L. T., Rissik, D., Letcher, R. A., & Jakeman, A. J. (2007). A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia. Environmental Modelling & Software, 22(8), 1129–1139. doi:10.1016/j.envsoft.2006.03.003.

    Article  Google Scholar 

  • van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., & Arellano Jr., A. F. (2006). Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics, 6(11), 3423–3441. doi:10.5194/acp-6-3423-2006.

    Article  Google Scholar 

  • Vasconcelos, M. J. P., Silva, S., Tome, M., Alvim, M., & Pereira, J. C. (2001). Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering and Remote Sensing, 67(1), 73–81.

    Google Scholar 

  • Venkataraman, C., Habib, G., Kadamba, D., Shrivastava, M., Leon, J. F., Crouzille, B., et al. (2006). Emissions from open biomass burning in India: integrating the inventory approach with high-resolution moderate resolution imaging spectroradiometer (MODIS) active-fire and land cover data. Global Biogeochemical Cycles, 20(2), 1–12. doi:10.1029/2005GB002547.

    Article  Google Scholar 

  • Weir, J. R. (2007). Using relative humidity to predict spotfire probability on prescribed burns. In R. E. Sosebee, D. B. Wester, C. M. Britton, E. D. McArthur, & S. G. Kitchen (Eds.), Proceedings: Shrubland dynamics—fire and water (pp. 69–72). USA: Proceedings RMRS-P-47.

  • Welty, L. J., Peng, R. D., Zeger, S. L., & Dominici, F. (2009). Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality. Biometrics, 65(1), 282–291. doi:10.1111/j.1541-0420.2007.01039.x.

    Article  CAS  Google Scholar 

  • Wiens, D. A., DeMets, C., Gordon, R. G., Stein, S., Argus, D., Engeln, J. F., et al. (1985). A diffuse plate boundary model for Indian Ocean tectonics. Geophysical Research Letters, 12(7), 429–432.

    Article  Google Scholar 

  • Yaghmaei, L., Soltani, S., & Khodagholi, M. (2009). Bioclimatic classification of Isfahan province using multivariate statistical methods. International Journal of Climatology, 29(12), 1850–1861. doi:10.1002/joc.1835.

    Article  Google Scholar 

  • Yang, J., He, H. S., Shifley, S. R., & Gustafson, E. J. (2007). Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest Science, 53(1), 1–15. doi:10.1142/9789812706713_0001.

    CAS  Google Scholar 

  • Zheng, Y., Cai, T., & Feng, Z. (2006). Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers. Biometrics, 62(1), 279–287. doi:10.1111/j.1541-0420.2005.00441.x.

    Article  Google Scholar 

  • Zumbrunnen, T., Menéndez, P., Bugmann, H., Conedera, M., Gimmi, U., & Bürgi, M. (2012). Human impacts on fire occurrence: a case study of hundred years of forest fires in a dry alpine valley in Switzerland. Regional Environmental Change, 12(4), 935–949. doi:10.1007/s10113-012-0307-4.

    Article  Google Scholar 

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Acknowledgments

This research was supported by Isfahan University of Technology. Authors greatly appreciate Forest, Range, and Watershed Organization of Iran for providing the data. We thank Dr. Majid Iravani from University of Alberta for his assistance and constructive comments.

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Authors and Affiliations

  1. Department of Natural Resources, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Hossein Bashari, Seyed Jamaleddin Khajeddin & Hamed Sangoony

  2. Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, 88186-34141, Iran

    Ali Asghar Naghipour & Pejman Tahmasebi

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  1. Hossein Bashari
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Correspondence to Hossein Bashari.

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Bashari, H., Naghipour, A.A., Khajeddin, S.J. et al. Risk of fire occurrence in arid and semi-arid ecosystems of Iran: an investigation using Bayesian belief networks. Environ Monit Assess 188, 531 (2016). https://doi.org/10.1007/s10661-016-5532-8

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