Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
Shopping Cart: ( empty )
You are here: Home > Journals > WF > WF05091
RESEARCH ARTICLE
Contents Vol 16(3)

Integrating new methods and tools in fire danger rating

Christos Vasilakos A , Kostas Kalabokidis B D , John Hatzopoulos A , George Kallos C and Yiannis Matsinos A
+ Author Affiliations
- Author Affiliations

A Department of Environmental Studies, University of the Aegean, University Hill, 81100 Mytilene, Greece.

B Department of Geography, University of the Aegean, University Hill, 81100 Mytilene, Greece.

C Department of Applied Physics, National and Kapodistrian University of Athens, Bldg PHYS-V, 15784 Athens, Greece.

D Corresponding author. Email: kalabokidis@aegean.gr

International Journal of Wildland Fire 16(3) 306-316 https://doi.org/10.1071/WF05091
Submitted: 3 October 2005  Accepted: 17 November 2006   Published: 3 July 2007

Abstract

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

Additional keywords: geo-informatics, GIS, natural hazards, neural networks, wildfires.


Acknowledgements

Funding for the present research was provided by the European Union within the RTD project ‘Automated Fire and Flood Hazard Protection System/AUTO-HAZARD PRO’ (EVG1-CT-2001–00057). The authors thank their colleagues at the Geography of Natural Disasters Laboratory at the University of the Aegean, and the Greek authorities (the Mytilene–Lesvos Fire Department and Forest Service, and the General Secretariat for Civil Protection) for their support and cooperation. We also acknowledge and appreciate the peer review on previous drafts by three anonymous reviewers of the journal and Dr Peter F. Moore of GHD, Australia.


References


Andrews PL, Bevins CD, Seli RC (2003) ‘BehavePlus fire modeling system user’s Guide, version 2.0.’ USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-106WWW. (Ogden, UT)

Bishop CM (1995) ‘Neural Networks for Pattern Recognition.’ (Oxford University Press: Oxford)

Carrega P (1991) A meteorological index of forest fire hazard in Mediterranean France. International Journal of Wildland Fire  1((2)), 79–86.
| Crossref | GoogleScholarGoogle Scholar | Deeming JE, Burgan RE, Cohen JD (1977) ‘The national fire-danger rating system – 1978.’ USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-39. (Ogden, UT)

Fausett L (1994) ‘Fundamentals of Neural Networks: Architecture, Algorithms and Applications.’ (Prentice-Hall, Inc: US)

Hampshire JBII, Pearlmutter BA (1990) Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function. In ‘Proceedings of the 1990 Connectionist Models Summer School’. (Eds D Touretzky, J Elman, T Sejnowski, G Hinton) (Morgan Kaufmann: CA).

Hoffmann AA, Schindler L, Goldammer JG (1999) Aspects of a fire information system for East Kalimantan, Indonesia. In ‘Proceedings of the 3rd International Symposium on Asian Tropical Forest Management’. 20–23 September 1999, Samarinda, East Kalimantan, Indonesia, pp. 176–185. (Eds H Suhartoyo, T Toma) (Tropical Forest Research Center, Mulawarman University and Japan International Cooperation Agency: Indonesia)

Jordan MI (1995) Why the logistic function? A tutorial discussion on probabilities and neural networks. MIT Computational Cognitive Science Report 9503, http://www.cs.berkeley.edu/~jordan.

Kalabokidis KD (2001) Forest fire prevention, problems and strategies. In ‘Proceedings of the 2001 AGROTICA Symposium on Forest Development and Protection’, 1 February 2001, Thessaloniki, pp. 13–19. (Ministry of Agriculture and NAGREF)

Kalabokidis KD (2004) Automated forest fire and flood hazard protection system. Disaster management: linking people and the environment. Geoinformatics  7, 14–17.
Kalabokidis K, Roussou O, Vasilakos C, Markopoulou D (2004) Spatial modeling of landscape fire fuels and behavior. In ‘Proceedings 7th Pan-Hellenic Geographical Conference of the Hellenic Geographical Society’, 14–17 October 2004, pp. 486–494. (University of the Aegean, Department of Geography)

Kallos G, Nickovic S, Jovic D, Kakaliagou O, Papadopoulos A, Misirlis N, Boukas L, Mimikou N (1997) The Eta model operational forecasting system and its parallel implementation. In ‘Proceedings of the 1st Workshop on Large-Scale Scientific Computations’, 7–11 June 1997, Varna, Bulgaria. (Eds M Griebel, OP Iliev, SD Mergenor, PS Vassilevski) Notes on Numerical Fluid Mechanics 62, 176–188.

Masters T (1993) ‘Practical Neural Networks Recipes in C++.’ (Academic Press: US)

Papadopoulos A, Katsafados P , Kallos G (2001) Regional weather forecasting for marine application. GAOS  8, 219–237.
Rumelhart DE, McClelland JL (1986) ‘Parallel Distributed Processing: Explorations in the Microstructure of Cognition.’ (The MIT Press: MA)

Saaty T (1980) ‘The Analytic Hierarchy Process.’ (McGraw-Hill: US)

Sarle WS (1997) Neural Network FAQ. Periodic posting to the Usenet newsgroup comp.ai.neural-nets. ftp://ftp.sas.com/pub/neural/FAQ.html [Verified August 2005]

Triantaphyllou E , Mann SH (1995) Using the analytic hierarchy process for decision making in engineering applications: some challenges. International Journal of Industrial Engineering: Applications and Practice  2, 35–44.
Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System, Canadian Forestry Service, Forestry Technical Report 35. (Ottawa, ON)

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


Viegas X, Bovio G, Ferreira A, Nosenzo A , Bernard S (1999) Comparative study of various methods of fire danger evaluation in Southern Europe. International Journal of Wildland Fire  9((4)), 235–246.
| Crossref | GoogleScholarGoogle Scholar |