Araştırma Makalesi

Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective

Cilt: 4 Sayı: 1 31 Ocak 2018
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Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective

Öz

Natural hazard assessments are core to risk definition and early warning systems and play a fundamental role in the prevention of major damages. Traditional hazard identification methods are static. For this reason, new information and conditions cannot be easily included in the pre-defined hazard assessments. The Bayesian Networks can be used effectively for dynamic hazard identification. In this study, a methodology based on the Bayesian Networks model is presented for dynamic avalanche hazard assessment, in which changed and renewed data can be included in the system. In the proposed methodology, the integration of the Bayesian Networks and Geographical Information Systems (GIS) is modeled in the National Spatial Data Infrastructure (NSDI) perspective. In this structure, it is possible to combine and analyze the data obtained from different sources and factors for avalanche hazard can be dynamically updated with real-time updated data and temporal hazard mapping can be produced. The proposed methodology provides a generic structure and has an attribute making it applicable for dynamic mapping studies for other disasters.

Anahtar Kelimeler

Kaynakça

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  3. Ames D.P., Anselmo A., (2008), Bayesian Network Integration with GIS, In: Encyclopedia of GIS, Springer US, pp. 39-45.
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  5. Annoni A., Craglia M., de Roo A., San-Miguel J., (2010), Earth observations and dynamic mapping: Key assets for risk management, Geographic Information and Cartography fore Risk and Crisis Management, In: Lecture Notes in Geoinformation and Cartography, (Konecny M., Zlatanova S., Bandrova T.L., Eds.), Springer-Verlag, Berlin-Heidelberg, pp.3-22.
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

İpek Yılmaz *
ONDOKUZ MAYIS ÜNİVERSİTESİ
Türkiye

Derya Öztürk
ONDOKUZ MAYIS ÜNİVERSİTESİ
Türkiye

Yayımlanma Tarihi

31 Ocak 2018

Gönderilme Tarihi

14 Aralık 2017

Kabul Tarihi

5 Şubat 2018

Yayımlandığı Sayı

Yıl 2018 Cilt: 4 Sayı: 1

Kaynak Göster

APA
Yılmaz, İ., & Öztürk, D. (2018). Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğal Afetler ve Çevre Dergisi, 4(1), 34-44. https://doi.org/10.21324/dacd.365255
AMA
1.Yılmaz İ, Öztürk D. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğ Afet Çev Derg. 2018;4(1):34-44. doi:10.21324/dacd.365255
Chicago
Yılmaz, İpek, ve Derya Öztürk. 2018. “Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”. Doğal Afetler ve Çevre Dergisi 4 (1): 34-44. https://doi.org/10.21324/dacd.365255.
EndNote
Yılmaz İ, Öztürk D (01 Ocak 2018) Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğal Afetler ve Çevre Dergisi 4 1 34–44.
IEEE
[1]İ. Yılmaz ve D. Öztürk, “Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”, Doğ Afet Çev Derg, c. 4, sy 1, ss. 34–44, Oca. 2018, doi: 10.21324/dacd.365255.
ISNAD
Yılmaz, İpek - Öztürk, Derya. “Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”. Doğal Afetler ve Çevre Dergisi 4/1 (01 Ocak 2018): 34-44. https://doi.org/10.21324/dacd.365255.
JAMA
1.Yılmaz İ, Öztürk D. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğ Afet Çev Derg. 2018;4:34–44.
MLA
Yılmaz, İpek, ve Derya Öztürk. “Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”. Doğal Afetler ve Çevre Dergisi, c. 4, sy 1, Ocak 2018, ss. 34-44, doi:10.21324/dacd.365255.
Vancouver
1.İpek Yılmaz, Derya Öztürk. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğ Afet Çev Derg. 01 Ocak 2018;4(1):34-4. doi:10.21324/dacd.365255

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