International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

P. Papakosta A C , G. Xanthopoulos B and D. Straub A
+ Author Affiliations
- Author Affiliations

A Engineering Risk Analysis Group, Technische Universitaet München, Theresienstrasse 90, 80333, Munich, Germany.

B Hellenic Agricultural Organisation ‘Demeter’, Institute of Mediterranean Forest Ecosystems, Terma Alkmanos, 11528, Athens, Greece.

C Corresponding author. Email: patty.papakosta@gmail.com

International Journal of Wildland Fire 26(1) 10-23 https://doi.org/10.1071/WF15113
Submitted: 10 June 2015  Accepted: 13 October 2016   Published: 11 January 2017

Abstract

Loss prediction models are an important part of wildfire risk assessment, but have received only limited attention in the scientific literature. Such models can support decision-making on preventive measures targeting fuels or potential ignition sources, on fire suppression, on mitigation of consequences and on effective allocation of funds. This paper presents a probabilistic model for predicting wildfire housing loss at the mesoscale (1 km2) using Bayesian network (BN) analysis. The BN enables the construction of an integrated model based on causal relationships among the influencing parameters jointly with the associated uncertainties. Input data and models are gathered from literature and expert knowledge to overcome the lack of housing loss data in the study area. Numerical investigations are carried out with spatiotemporal datasets for the Mediterranean island of Cyprus. The BN is coupled with a geographic information system (GIS) and the resulting estimated house damages for a given fire hazard are shown in maps. The BN model can be attached to a wildfire hazard model to determine wildfire risk in a spatially explicit manner. The developed model is specific to areas with house characteristics similar to those found in Cyprus, but the general methodology is transferable to any other area, as well as other damages.

Additional keywords: loss prediction, Mediterranean, vulnerability.


References

Ager AA, Vaillant NM, Finney MA (2010) A comparison of landscape fuel treatment strategies to mitigate wildland fire risk in the urban interface and preserve old forest structure. Forest Ecology and Management 259, 1556–1570.
A comparison of landscape fuel treatment strategies to mitigate wildland fire risk in the urban interface and preserve old forest structure.CrossRef | open url image1

Aspinall WP, Woo G, Voight B, Baxter PJ (2003) Evidence-based volcanology: application to eruption crises. Journal of Volcanology and Geothermal Research 128, 273–285.
Evidence-based volcanology: application to eruption crises.CrossRef | 1:CAS:528:DC%2BD3sXovF2qt78%3D&md5=4608a8cd335eebc665a8c2265ad42bafCAS | open url image1

Bayraktarli YY, Ulfkjaer J, Yazgan U, Faber MH (2005) On the application of Bayesian probabilistic networks for earthquake risk management. In ‘Proceedings of ICOSSAR 05 – Safety and reliability of engineering’, 19–23 June 2005, Rome, Italy. (Eds G Augusti, G Schueller, M Ciampoli) pp. 20–23 (Millpress: Rome).

Bensi MT, Der Kiureghian A, Straub D (2014) Framework for post-earthquake risk assessment and decision-making for infrastructure systems. ASCE–ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 1, 04014003

Biedermann A, Taroni F, Delemont O, Semadeni C, Davison A (2005) The evaluation of evidence in the forensic investigation of fire incidents (Part I): an approach using Bayesian networks. Forensic Science International 147, 49–57.
The evaluation of evidence in the forensic investigation of fire incidents (Part I): an approach using Bayesian networks.CrossRef | 1:STN:280:DC%2BD2crmvFGntA%3D%3D&md5=2398831285820c0918a182ed2870b696CAS | open url image1

Birkmann J (2006) Indicators and criteria for measuring vulnerability: theoretical bases and requirements. In ‘Measuring vulnerability to natural hazards’. (Ed. J Birkmann) pp. 55–77. (United Nations University Press: Tokyo).

Blanchi R, Lucas C, Leonard J, Finkele K (2010) Meteorological conditions and wildfire-related house loss in Australia. International Journal of Wildland Fire 19, 914–926.
Meteorological conditions and wildfire-related house loss in Australia.CrossRef | open url image1

Blaser L, Ohrnberger M, Riggelsen C, Scherbaum F (2009) Bayesian belief network for tsunami warning decision support. In ‘Proceedings of the 10th European conference on symbolic and quantitative approaches to reasoning with uncertainty (ECSQARU 2009), 1–3 July 2009, Verona, Italy. (Eds C Sossai, G Chemello) pp. 757–786. (Springer: Berlin)

Blong R (2003) A new damage index. Natural Hazards 30, 1–23.
A new damage index.CrossRef | open url image1

Byram GM (1959) Combustion of forest fuels. In ‘Forest fire: control and use’. (Ed. KP Davis) pp. 61–89. (McGraw-Hill: New York)

Butler BW, Cohen JD (1998) Firefighter safety zones: a theoretical model based on radiative heating. International Journal of Wildland Fire 8, 73–77.
Firefighter safety zones: a theoretical model based on radiative heating.CrossRef | open url image1

Cardona O-D, van Aalst MK, Birkmann J, Fordham M, McGregor G, Mechler R (2012) Determinants of risk: exposure and vulnerability In ‘Managing the risks of extreme events and disasters to advance climate change adaptation’. (Eds CB Field, V Barros, TF Stocker, D Qin, DJ Dokken, KL Ebi, MD Mastrandrea, KJ Mach, G-K Plattner, SK Allen, M Tignor, PM Midgley) A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) pp. 65–108 (Cambridge University Press: Cambridge, UK).

Carreño M-L, Cardona OD, Barbat AH (2007) Urban seismic risk evaluation: a holistic approach. Natural Hazards 40, 137–172.
Urban seismic risk evaluation: a holistic approach.CrossRef | open url image1

Cheng H, Hadjisophocleous GV (2009) The modeling of fire spread in buildings by Bayesian network. Fire Safety Journal 44, 901–908.
The modeling of fire spread in buildings by Bayesian network.CrossRef | open url image1

Cohen JD (2000) Preventing disaster: home ignitability in the wildland–urban interface. Journal of Forestry 98, 15–21.

Cohen JD (2004) Relating flame radiation to home ignition using modeling and experimental crown fires. Canadian Journal of Forest Research 34, 1616–1626.
Relating flame radiation to home ignition using modeling and experimental crown fires.CrossRef | open url image1

Dahmani Y, Hamri ME (2011) Event triggering estimation for Cell-DEVS: wildfire spread simulation case. In ‘Proceedings of the UKSim 5th European symposium on computer modeling and simulation (EMS), 2011’, 16–18 November 2011, Madrid, Spain. (Ed. NJ Piscataway) (IEEE Computer Society: Washington, DC)

Dlamini WM (2010) A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland. Environmental Modelling & Software 25, 199–208.
A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland.CrossRef | open url image1

Dowdy AJ, Mills GA, Finkele K, de Groot W (2010) Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index. Meteorological Applications 17, 298–312.

ECONorthwest (2007) Linn County community wildfire protection plan. Available at https://scholarsbank.uoregon.edu/xmlui/bitstream/handle/1794/5795/Linn_County_Wildfire_Plan.pdf?sequence=1 [Verified 14 November 2016]

ESRI (2012) ‘ArcGIS 10.1. ESRI.’ (Environmental Systems Resource Institute: Redlands, CA)

Finney MA (2005) The challenge of quantitative risk analysis for wildlife fire. Forest Ecology and Management 211, 97–108.
The challenge of quantitative risk analysis for wildlife fire.CrossRef | open url image1

Finney MA (2006) An overview of FlamMap fire modeling capabilities. In ‘Fuels management: how to measure success: conference proceedings’, 28–30 March 2006, Portland, OR. USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-41, pp. 213–220. (Fort Collins, CO)

FSBC (2003) The home-owners firesmart manual – protect your home from wildfire (British Columbia Ministry of Forests, BC) Available at http://www.embc.gov.bc.ca/ofc/interface/pdf/homeowner-firesmart.pdf [Verified 14 November 2016]

Gibbons P, Van Bommel L, Gill AM, Cary GJ, Driscoll DA, Bradstock RA, Knight E, Moritz MA, Stephens SL, Lindenmayer DB (2012) Land-management practices associated with house loss in wildfires. PLoS One 7, e29212
Land-management practices associated with house loss in wildfires.CrossRef | 1:CAS:528:DC%2BC38XhvVyktbY%3D&md5=904190e7a8a85396c34f32aed5b055a3CAS | open url image1

Grêt-Regamey A, Straub D (2006) Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS. Natural Hazards and Earth System Sciences 6, 911–926.
Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS.CrossRef | open url image1

Guzzetti F, Salvati P, Stark CP (2005) Evaluation of risk to the population posed by natural hazards in Italy. In ‘Landslide risk management’. (Eds O Hungr, R Fell, R Couture, E Eberhardt) pp. 381–389. (Taylor & Francis Group: London)

Hanea D, Ale B (2009) Risk of human fatality in building fires: a decision tool using Bayesian networks. Fire Safety Journal 44, 704–710.
Risk of human fatality in building fires: a decision tool using Bayesian networks.CrossRef | open url image1

Hardy CC (2005) Wildland fire hazard and risk: problems, definitions, and context. Forest Ecology and Management 211, 73–82.
Wildland fire hazard and risk: problems, definitions, and context.CrossRef | open url image1

Harris S, Anderson W, Kilinc M, Fogarty L (2012) The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale. Natural Hazards 63, 391–415.
The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale.CrossRef | open url image1

Howes AL, Maron M, Mcalpine CA (2010) Bayesian networks and adaptive management of wildlife habitat. Conservation Biology 24, 974–983.
Bayesian networks and adaptive management of wildlife habitat.CrossRef | open url image1

Jensen FV, Nielsen TD (2007) ‘Bayesian networks and decision graphs.’ (Springer: New York).

Kjaerulff UB, Madsen AL (2013) ‘Bayesian networks and influence diagrams: a guide to construction and analysis.’ (Springer: New York).

Koo E, Pagni PJ, Weise DR, Woycheese JP (2010) Firebrands and spotting ignition in large-scale fires. International Journal of Wildland Fire 19, 818–843.
Firebrands and spotting ignition in large-scale fires.CrossRef | open url image1

Kron W (2002) Keynote lecture: flood risk = hazard × exposure × vulnerability. In ‘Proceedings of the flood defence (IFSD 2002)’, 10–13 September 2002, Beijing, China. (Eds B Wu, Z-Y Wang, G Wang, GHG Huang, H Fang, J Huang) pp. 82–97. (Science Press: New York)

Lawson BD, Armitage OB (2008) Weather guide for the Canadian Forest Fire Danger Rating System. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB. Catalogue no. Fo134-8/2008E-PDF.

Li L, Wang J, Leung H, Zhao S (2012) A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk. Risk Analysis 32, 1072–1092.
A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk.CrossRef | open url image1

Long A, Randall C (2004) Wildfire risk assessment guide for homeowners (in the southern United States). School of Forest Resources and Conservation, University of Florida, Institute of Food and Agricultural Sciences (IFAS). Available at https://www.bugwood.org/acrobat/WildfireRAGH.pdf [Verified 14 November 2016]

Lynch DL (2004) What do forest fires really cost? Journal of Forestry 102, 42–49.

McArthur A (1967) Fire behaviour in eucalypt forests. Commonwealth of Australia, Forestry and Timber Bureau Leaflet 107. (Canberra, ACT).

Mell WE, Manzello SL, Maranghides A, Butry D, Rehm RG (2010) The wildland–urban interface fire problem – current approaches and research needs. International Journal of Wildland Fire 19, 238–251.
The wildland–urban interface fire problem – current approaches and research needs.CrossRef | open url image1

Miller C, Ager AA (2013) A review of recent advances in risk analysis for wildfire management International Journal of Wildland Fire 22, 1–14.
A review of recent advances in risk analysis for wildfire managementCrossRef | open url image1

Mitsopoulos I, Mallinis G, Arianoutsou M (2015) Wildfire risk assessment in a typical Mediterranean wildland–urban interface of Greece. Environmental management 55, 900–915.
Wildfire risk assessment in a typical Mediterranean wildland–urban interface of Greece.CrossRef | open url image1

Mozumder P, Helton R, Berrens RP (2009) Provision of a wildfire risk map: informing residents in the wildland–urban interface. Risk Analysis 29, 1588–1600.
Provision of a wildfire risk map: informing residents in the wildland–urban interface.CrossRef | open url image1

Nemry F, Uihlein A (2008) Environmental improvement potentials of residential buildings (IMPRO-Building). Joint Research Centre (JRC) scientific and technical series, European Commission, Spain, EUR 23493 EN. Office for Official Publications of the European Communities, Luxembourg.

Oregon Forestry Department (OFD) (2004) Wildfire risk explorer: identifying and assessment of communities at risk in Oregon, draft version 4.0. (Oregon Department of Forestry: Salem, OR)

Ohlson DW, Blackwell BA, Hawkes BC, Bonin D (2003) A wildfire risk management system – an evolution of the wildfire threat rating system. In ‘Proceedings of the 3rd international wildland fire conference and exhibition’, 3–6 October 2003, Sydney, NSW. Available at http://www.fire.uni-freiburg.de/summit-2003/3-IWFC/Papers/3-IWFC-131-Ohlson.pdf [Verified 15 November 2016]

Papakosta P (2015) Bayesian network models for wildfire risk estimation in the Mediterranean basin. PhD thesis, Technische Universitaet München, Germany.

Papakosta P, Straub D (2013) A Bayesian network approach to assessing wildfire consequences. In ‘Proceedings of the 11th international conference on structural safety and reliability’. (Eds G Deodatis, BR Ellingwood, DM Frangopol) pp. 3131–3138. (Taylor & Francis Group: London).

Papakosta P, Straub D (2015) Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatiotemporal data. iForest
Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatiotemporal data.CrossRef | open url image1

Papakosta P, Öster J, Scherb A, Straub D (2013) Fire occurrence prediction in the Mediterranean: application to southern France. In ‘EGU General Assembly conference abstracts’, 7–12 April 2013, Vienna, Austria. Vol. 15: 6336.

Papakosta P, Scherb A, Zwirglmaier K, Straub D (2014) Estimating daily fire risk in the mesoscale by means of a Bayesian network model and a coupled GIS. In ‘Proceedings of the vii international conference on forest fire research: advances in forest fire research’, 17–20 November 2014, Coimbra, Portugal. (Ed. DX Viegas) (ADAI/CEIF, University of Coimbra: Coimbra, Portugal).

Paul BK (2011) ‘Environmental hazards and disasters: contexts, perspectives and management.’ (John Wiley & Sons: Hoboken, NJ).

Penman TD, Price O, Bradstock RA (2011) Bayes Nets as a method for analysing the influence of management actions in fire planning. International Journal of Wildland Fire 20, 909–920.
Bayes Nets as a method for analysing the influence of management actions in fire planning.CrossRef | open url image1

Penman TD, Eriksen C, Blanchi R, Chladil M, Gill AM, Haynes K, Leonard J, McLennan J, Bradstock RA (2013) Defining adequate means of residents to prepare property for protection from wildfire. International Journal of Disaster Risk Reduction 6, 67–77.
Defining adequate means of residents to prepare property for protection from wildfire.CrossRef | open url image1

Penman TD, Bradstock RA, Price OF (2014) Reducing wildfire risk to urban developments: simulation of cost-effective fuel treatment solutions in south-eastern Australia. Environmental Modelling & Software 52, 166–175.
Reducing wildfire risk to urban developments: simulation of cost-effective fuel treatment solutions in south-eastern Australia.CrossRef | open url image1

Plucinski M (2012) Factors affecting containment area and time of Australian forest fires featuring aerial suppression. Forest Science 58, 390–398.
Factors affecting containment area and time of Australian forest fires featuring aerial suppression.CrossRef | open url image1

Plucinski M, McCarthy G, Hollis J, Gould J (2012) The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel. International Journal of Wildland Fire 21, 219–229.
The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel.CrossRef | open url image1

Rothermel RC, Deeming JE (1980) Measuring and interpreting fire behavior for correlation with fire effects. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-93. (Ogden, UT)

Salis M, Ager AA, Arca B, Finney MA, Bacciu V, Duce P, Spano D (2013) Assessing exposure of human and ecological values to wildfire in Sardinia, Italy. International Journal of Wildland Fire 22, 549–565.
Assessing exposure of human and ecological values to wildfire in Sardinia, Italy.CrossRef | 1:CAS:528:DC%2BC3sXpvFakt7k%3D&md5=56877e2504fc93d910712df09ed82afaCAS | open url image1

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) ‘Global sensitivity analysis: the primer.’ (John Wiley & Sons: West Sussex, England).

Scott JH (2006). Comparison of crown fire modeling systems used in three fire management applications. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-58. Available at http://www.fs.fed.us/rm/pubs/rmrs_rp058.pdf [Verified 17 November 2016]

Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Computers & Geosciences 42, 189–199.
Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China.CrossRef | open url image1

Statistical Service (2010) Construction and housing statistics. Available at http://www.mof.gov.cy/mof/cystat/statistics.nsf/All/02762FF6F1F58664C2257B8E002FC1BA/$file/CONSTRUCTION_AND_HOUSING-2010-060312.pdf?OpenElement [Verified 15 November 2016]

Statistical Service (2011) Conventional dwellings enumerated by year of construction (completion). Report table statistics. Available at http://www.mof.gov.cy/mof/cystat/statistics.nsf/populationcondition_22main_en/populationcondition_22main_en?OpenForm&sub=2&sel=2 [Verified 15 November 2016]

Straub D (2005) Natural hazards risk assessment using Bayesian networks. In ‘Proceedings of ICOSSAR 05 – Safety and reliability of engineering systems and structures’, 19–23 June 2005, Rome, Italy. (Eds G Augusti, G Schueller, M Ciampoli) pp. 2535–2542 (Millpress: Rotterdam, Netherlands)

Straub D, Der Kiureghian A (2010) Bayesian network enhanced with structural reliability methods: methodology. Journal of Engineering Mechanics 136, 1248–1258.
Bayesian network enhanced with structural reliability methods: methodology.CrossRef | open url image1

Sunderman SO, Weisberg PJ (2012) Predictive modelling of burn probability and burn severity in a desert spring ecosystem. International Journal of Wildland Fire 21, 1014–1024.
Predictive modelling of burn probability and burn severity in a desert spring ecosystem.CrossRef | open url image1

Syphard AD, Keeley JE, Massada AB, Brennan TJ, Radeloff VC (2012) Housing arrangement and location determine the likelihood of housing loss due to wildfire. PLoS One 7, e33954
Housing arrangement and location determine the likelihood of housing loss due to wildfire.CrossRef | 1:CAS:528:DC%2BC38XltlOit7w%3D&md5=66379074a13b8e411739faef98c5f26fCAS | open url image1

Tutsch M, Haider W, Beardmore B, Lertzman K, Cooper AB, Walker RC (2010) Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern Gulf Islands, British Columbia, Canada. Canadian Journal of Forest Research 40, 2104–2114.
Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern Gulf Islands, British Columbia, Canada.CrossRef | open url image1

Office of the United Nations Disaster Relief Co-ordinator (UNDRO) (1980) Natural disasters and vulnerability analysis: report of experts group meeting (9–12 July 1979). (Nabu Press: Charleston, SC).

Vogel K, Riggelsen C, Scherbaum F (2013) Challenges for Bayesian Network learning in a flood damage assessment application. In ‘Proceedings of ICOSSAR 13 – Safety and reliability of engineering’, 16–20 June 2013. (Columbia University: New York)

Xanthopoulos G (2004) Who should be responsible for forest fires? Lessons from the Greek experience. In ‘Proceedings of the second international symposium on fire economics, planning and policy: a global view’, 19–22 April, Cordoba, Spain. (Ed. A. González-Cabán) USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW-GTR-208. (Albany, CA)

Xanthopoulos G (2008) Parallel lines. Wildfire 17, 8–20.

Xanthopoulos G, Calfapietra C, Fernandes P (2012) Fire hazard and flammability of European forest types. In ‘Post-fire management and restoration of southern European forests’. (Eds F Moreira, M Arianoutsou, P Corona, J De las Heras) pp. 79–92. (Springer: Dordrecht, Netherlands)

Xanthopoulos G, Roussos A, Giannakopoulos C, Karali A, Hatzaki M (2014) Investigation of the weather conditions leading to large forest fires in the area around Athens, Greece. In ‘Proceedings of the VII international conference on forest fire research: advances in forest fire research’, 17–20 November 2014, Coimbra, Portugal. (Ed. DX Viegas) (ADAI/CEIF, University of Coimbra: Coimbra, Portugal)

Zwirglmaier K, Papakosta P, Straub D (2013) Learning a Bayesian network model for predicting wildfire behavior. In ‘Proceedings of the 11th international conference on structural safety and reliability’. (Eds G Deodatis, BR Ellingwood and DM Frangopol) (Taylor & Francis Group: London)


Full Text PDF (1.1 MB) Export Citation