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Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi

Year 2020, Volume: 6 Issue: 2, 288 - 303, 01.07.2020
https://doi.org/10.21324/dacd.645701

Abstract

Orman yangın organizasyonları, yangınlarla mücadelede başarılı olabilmek için doğru, güncel ve kolaylıkla elde edilebilir bilgilere ihtiyaç duyar. Bu bilgilerin temini, yangın risk ve tehlike potansiyeli ile yangın davranışı hakkında doğru ve hızlı bilgi verebilen kapsamlı bir sistemin varlığı ile mümkün olabilir. Bu çalışmada, Türkiye orman yangın organizasyonu ve yangın yönetim planlamalarında kullanılabilecek bir Karar Destek Sisteminin (KDS) kavramsal çerçevesi, yapısı ve hiyerarşisi belirlenmiştir. KDS tasarımında, bir bütün halinde veya ayrı ayrı çalışabilecek dört alt sistem öngörülmüştür. Bunlar; i) Yangın Bilgi Sistemi; orman yangınları ile ilgili mekânsal, zamansal ve tanımlayıcı bilgilerin depolandığı ve veriler üzerinden akıllı sorgulamaların yapılabildiği alt sistem, ii) Yangın Çıkma İhtimalini Tahmin Sistemi; sabit ve değişken çevre faktörlerini dikkate alan ve belirli bir alan için yangın risk ve tehlike potansiyelini derecelendiren alt sistem, iii) Meteorolojik Yangın İndeksi Sistemi; yanıcı madde nemine bağlı olarak yanıcı maddelerin tutuşabilirliğini belirleyen ve yangının başlaması durumunda potansiyel yangın yayılma oranı ve yangın şiddeti hakkında bilgi veren alt sistem ve iv) Yangın Davranışı Tahmin Sistemi; yangın davranışını farklı hava halleri, topoğrafya ve yanıcı madde özelliklerine bağlı olarak tahmin etmeye çalışan alt sistemidir. Geliştirilen sistem tasarımı uygulamada kullanılabilecek şekilde hazır hale geldiğinde, orman yangınları ile mücadelede yangın yöneticilerine yangın öncesi, yangınla mücadele ve yangın sonrası planlamalarda önemli katkılar sunacak potansiyeldedir.

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Conceptual Framework of an Effective Decision Support System in Forest Fire Management

Year 2020, Volume: 6 Issue: 2, 288 - 303, 01.07.2020
https://doi.org/10.21324/dacd.645701

Abstract

Forest fire management organizations require accurate, timely and readily available information to be successful in forest fire suppression. A sound decision support system is necessary to acquire and make this information available to fire management organization. In this study, the framework, structure and hierarchy of a Decision Support System (DSS) was designed in forest fire management organization and planning in Turkey. DSS consists of four subsystems which can operate individually or collectively. These subsystems are; i) Fire Information System; a smart database for fire managers to input, store, update and manage data easily, ii) Fire Occurrence Prediction System; forest fire risk and danger determiner using constant environmental variables iii) Fire Weather Index System; fire weather index calculator for the ignition potential of fuels, potential rate of spread and fire intensity, iv) Fire Behavior Prediction System; fire propagation simulator for predicting fire behavior under varying weather, fuel and topographic conditions. When readily available for operational use, the DSS will have the potential to provide fire managers with tools and support necessary for successful fire prevention/presuppression, suppression and post fire management planning.

References

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  • Bilgili E., (1999), The Use of Decision Support Systems in Fire Management Planning, Orman Yangınlarının Önlenmesi ve Mücadelesi Semineri, 18-22 Mayıs, Fethiye, Muğla.
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  • Bilgili E., Sağlam B., (2003), Fire behavior in maquis fuels in Turkey, Forest Ecology and Management, 184(1-3), 201-207.
  • Bilgili E., Sağlam B., Başkent E.Z., (2001), Yangın amenajmani planlamalarinda yangin tehlike oranlari ve Coğrafi Bilgi Sistemleri, KSÜ Fen ve Mühendislik Dergisi, 4(2), 288-297.
  • Boer M.M., Sadler R.J., Wittkuhn R.S., McCaw L., Grierson P.F., (2009), Long-term impacts of prescribed burning on regional extent and incidence of wildfires—Evidence from 50 years of active fire management in SW Australian forests, Forest Ecology and Management, 259(1), 132-142.
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  • Carlson J.D., Bradshaw L.S., Nelson R.M., Bensch R.R., Jabrzemski R., (2007), Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with National Fire Danger Rating System algorithms, International Journal of Wildland Fire, 16(2), 204-216.
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  • Çanakçıoğlu, H., (1985) Orman Koruma, İstanbul Üniversitesi Orman Fakültesi Yayınların, İstanbul, Türkiye, 486ss.
  • Çanakçıoğlu, H., (1988), Yangın Tehlike İndekslerinden Yararlanılan Alanlar, Türkiye Ormanlarını Yangından Koruma Semineri, 1-4 Mayıs, Ankara, ss.153-162.
  • de Groot, W.J., Field R.D., Brady M.A., Roswintiarti O., Mohamad M., (2006), Development of the Indonesian and Malaysian Fire Danger Rating Systems, Mitigation and Adaptation Strategies for Global Change, 12, 165–180.
  • de Groot, W.J., Goldammer G.J., Keenan T., Brady M., Lynham T.J., Justice C.O., Csiszar, I.A., O'Loughlin K. (2006), Developing a global early warning system for wildland fire, 5th International Conference on Forest Fire Research, Figueira da Foz, Portugal 12.
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  • del Hoyo L.V., Isabel M.P.M., Vega F.J.M., (2011), Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data, European Journal of Forest Research, 130(6), 983-996.
  • Dimitrakopoulos A.P., Bemmerzouk A.M., (2003), Predicting live herbaceous moisture content from a seasonal drought index, International Journal of Biometeorology, 47, 73–79.
  • Dimitrakopoulos A.P., Bemmerzouk A.M., Mitsopoulos I.D., (2011), Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment, Meteorological Applications, 18(1), 83-93.
  • Fogarty L.G., Pearce H.G., Catchpole W.R., Alexander M.E., (1998), Adoption vs Adaptation: Lessons from applying the Canadian Forest Fire Danger Rating System in New Zealand. III International Conference on Forest Fire Research, 14th Conference on Fire and Forest Meteorology, 16–20 November, Luso, Portugal.
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There are 84 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Kadir Alperen Coskuner 0000-0001-5249-1604

Ertuğrul Bilgili 0000-0003-1006-4991

Publication Date July 1, 2020
Submission Date November 12, 2019
Acceptance Date January 9, 2020
Published in Issue Year 2020Volume: 6 Issue: 2

Cite

APA Coskuner, K. A., & Bilgili, E. (2020). Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi. Doğal Afetler Ve Çevre Dergisi, 6(2), 288-303. https://doi.org/10.21324/dacd.645701
AMA Coskuner KA, Bilgili E. Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi. J Nat Haz Environ. July 2020;6(2):288-303. doi:10.21324/dacd.645701
Chicago Coskuner, Kadir Alperen, and Ertuğrul Bilgili. “Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi”. Doğal Afetler Ve Çevre Dergisi 6, no. 2 (July 2020): 288-303. https://doi.org/10.21324/dacd.645701.
EndNote Coskuner KA, Bilgili E (July 1, 2020) Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi. Doğal Afetler ve Çevre Dergisi 6 2 288–303.
IEEE K. A. Coskuner and E. Bilgili, “Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi”, J Nat Haz Environ, vol. 6, no. 2, pp. 288–303, 2020, doi: 10.21324/dacd.645701.
ISNAD Coskuner, Kadir Alperen - Bilgili, Ertuğrul. “Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi”. Doğal Afetler ve Çevre Dergisi 6/2 (July 2020), 288-303. https://doi.org/10.21324/dacd.645701.
JAMA Coskuner KA, Bilgili E. Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi. J Nat Haz Environ. 2020;6:288–303.
MLA Coskuner, Kadir Alperen and Ertuğrul Bilgili. “Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi”. Doğal Afetler Ve Çevre Dergisi, vol. 6, no. 2, 2020, pp. 288-03, doi:10.21324/dacd.645701.
Vancouver Coskuner KA, Bilgili E. Orman Yangın Yönetiminde Etkili Bir Karar Destek Sisteminin Kavramsal Çerçevesi. J Nat Haz Environ. 2020;6(2):288-303.