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

Year 2018, Volume: 4 Issue: 1, 34 - 44, 31.01.2018
https://doi.org/10.21324/dacd.365255

Abstract

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.

References

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Dinamik Çığ Tehlike Değerlendirmesi İçin Bayes Ağlarının CBS'ye Entegrasyonu: UKVA Perspektifi

Year 2018, Volume: 4 Issue: 1, 34 - 44, 31.01.2018
https://doi.org/10.21324/dacd.365255

Abstract

Doğal
afetlerle ilgili çalışmalarda tehlike değerlendirmesi, risk tanımlama ve erken
uyarı sistemlerinin temelidir ve büyük kayıpların engellenmesinde önemli bir
rol oynamaktadır. Klasik tehlike tanımlama yöntemleri statiktir. Bu nedenle,
yeni bilgi ve koşullar önceden tanımlanmış tehlike değerlendirmelerine kolayca
dahil edilemez. Bayes Ağları, dinamik tehlike tanımlaması için etkin bir
şekilde kullanılabilir. Bu çalışmada, değişen ve yenilenen verilerin sisteme
dahil edilebildiği dinamik çığ tehlike değerlendirmesi için Bayes Ağlarına
dayanan bir yaklaşım sunulmuştur. Önerilen metodolojide, Bayes Ağlarının ve
Coğrafi Bilgi Sistemlerinin (CBS) entegrasyonu, Ulusal Konumsal Veri Altyapısı
(UKVA) perspektifinde modellenmiştir. Bu yapıda, farklı kaynaklardan elde
edilen verilerin birleştirilmesi ve analiz edilmesi mümkün olup, çığ tehlikesi
için etken faktörler gerçek zamanlı güncel verilerle dinamik olarak
güncellenerek zamansal tehlike haritaları üretilebilir. Önerilen metodoloji
genel bir yapı sunmaktadır ve diğer afetlere yönelik dinamik harita üretimi
çalışmaları için uyarlanabilir niteliktedir.




References

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  • Anderson-Berry L., King D., (2005), Mitigation of the impact of tropical cyclones in Northern Australia through community capacity enhancement, Mitigation and Adaptation Strategies for Global Change, 10(3), 367-392.
  • 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.
  • Aydın A., Eker R., (2017), GIS-Based snow avalanche hazard mapping: Bayburt-Aşağı Dere catchment case, Journal of Environmental Biology, 38, 937-943, doi: 10.22438/jeb/38/5(SI)/GM-10.
  • Bajpai N., (2009), Business Statistics, Pearson Education, 794 p.
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  • Bostancı H.T., Cömert Ç., Akıncı H., (2007), UKVA için tapu ve kadastro web servislerinin tasarımı ve geliştirilmesi, TMMOB Harita ve Kadastro Mühendisleri Odası 11. Türkiye Harita Bilimsel ve Teknik Kurultayı, 2-6 Nisan 2007, Ankara.
  • Bröring A., Echterhoff J., Jirka S., Simonis I., Everding T., Stasch C., Liang S., Lemmens R., (2011), New generation sensor web enablement, Sensors, 11, 2652-2699, doi:10.3390/s110302652.
  • Brugnot G., (2008), Spatial Management of Risks, ISTE Ltd and John Wiley & Sons Inc, London United Kingdom, 256 p.
  • Cookler L., Orton B., (2004), Developing a GIS avalanche forecasting model using real-time weather telemetry information for the south side of MT. Hood, Proceedings of the 2004 International Snow Science Workshop, Jackson Hole, Wyoming, pp. 145-152.
  • Cordy P., McClung D.M., Hawkins C.J., Tweedy J., Weick T., (2009), Computer assisted avalanche prediction using electronic weather sensor data, Cold Regions Science and Technology, 59, 227-233, doi: 10.1016/j.coldregions.2009.07.006.
  • Covăsnianu A., Grigoras I.R., State L.E., Balin I., Balin D., Hogas S., (2011), Mapping snow avalanche risk using GIS technique and 3D modeling: Case study Ceahlau National Park, Romanian Journal of Physics, 56(3-4), 476-483,doi: 10.2139/ssrn.1884082.
  • Cömert Ç., Akıncı H., (2005), Ulusal konumsal veri altyapısı ve e-Türkiye için önemi, TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı, 28 Mart-1 Nisan 2005, Ankara.
  • Çinicioğlu E.N., Ekici Ş.E., Ülengin F., (2015), Bayes ağ yapısının oluşturulmasında farklı yaklaşımlar: Nedensel Bayes ağları ve veriden ağ öğrenme, In: Sn. Prof. Dr. Halil Sarıaslan'a Armağan Kitabı, Siyasal Kitabevi, Ankara, pp.267-284.
  • d'Acremont M., Schultz W., Bossaerts P., (2013), The human brain encodes event frequencies while forming subjective belief, Journal of Neuroscience, 33(26), 10887-10897, doi: 10.1523/JNEUROSCI.5829-12.2013.
  • Doğan S., Akıncı H., Kılıçoğlu C., (2012), Bayes olasılık teoremi kullanılarak Samsun il merkezinin heyelan duyarlılık haritasının üretilmesi, 65. Türkiye Jeoloji Kurultayı, 2-6 Nisan 2012, Ankara.
  • Eckert N., Naaim M., Parent E., (2010), Long-term avalanche hazard assessment with a Bayesian depth-averaged propagation model, Journal of Glaciology, 56(198), 563-586, doi:10.3189/002214310793146331.
  • Elibüyük M., Yılmaz E., (2010), Türkiye’nin coğrafi bölge ve bölümlerine göre yükselti basamakları ve eğim grupları, Coğrafi Bilimler Dergisi, 8(1), 27-55.
  • Elmastaş N., Özcanlı M., (2011), Bitlis ilinde çığ afet alanlarının tespiti ve çığ risk analizi, VI.Ulusal Coğrafya Sempozyumu, 3-5 Kasım 2010, Ankara, Bildiriler Kitabı, pp. 303-314.
  • Germain D., (2016), Snow avalanche hazard assessment and risk management in northern Quebec, eastern Canada, Natural Hazards, 80, 1303-1321, doi: 10.1007/s11069-015-2024-z.
  • Grêt-Regamey A., Straub D., (2006), Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS, Natural Hazards and Earth System Sciences, 6(6), 911-926, doi:10.5194/nhess-6-911-2006.
  • Helbig N., van Herwijnen A., Jonas T., (2015), Forecasting wet-snow avalanche probability in mountainous terrain, Cold Regions Science and Technology, 120, 219-226, doi: 10.1016/j.coldregions.2015.07.001.
  • Hwang J.W., Lee Y.S., Cho S.B., (2011), Structure evolution of dynamic Bayesian network for traffic accident detection. In Evolutionary Computation (CEC), 2011 IEEE Congress on (pp. 1655-1671), IEEE (2011, June).
  • Jaedicke C., Syre E., Sverdrup-Thygeson K., (2014), GIS-aided avalanche warning in Norway, Computers & Geosciences, 66, 31-39, doi: 10.1016/j.cageo.2014.01.004.
  • Jebb A.T., (2017), Bayesian statistics, In: The SAGE Encyclopedia of Industrial and Organizational Psychology, (Rogelberg S.G., Ed.), SAGE Publications, Inc.
  • Jonkman N.S., Gerritsen H., Marchand M., (2012), Coastal storm, In: Handbook of Hazards and Disaster Risk Reduction and Management, (Wisner B., Gaillard J.C., Kelman I., Eds.), Taylor & Francis, New York, pp. 220-231.
  • Kadıoğlu M., (2008), Sel, heyelan ve çığ için risk yönetimi, TMMOB İnşaat Mühendisleri Odası Samsun Şubesi Sel-Heyelan-Çığ Sempozyumu, 28-29 Mayıs 2008, Samsun.
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  • Kragt M.E., (2009), A beginners guide to Bayesian network modelling for integrated catchment management. Landscape Logic, Technical Report No. 9, 22 p.
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There are 71 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İpek Yılmaz

Derya Öztürk

Publication Date January 31, 2018
Submission Date December 14, 2017
Acceptance Date February 5, 2018
Published in Issue Year 2018Volume: 4 Issue: 1

Cite

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 Yılmaz İ, Öztürk D. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. J Nat Haz Environ. January 2018;4(1):34-44. doi:10.21324/dacd.365255
Chicago Yılmaz, İpek, and Derya Öztürk. “Integration of Bayesian Networks With GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”. Doğal Afetler Ve Çevre Dergisi 4, no. 1 (January 2018): 34-44. https://doi.org/10.21324/dacd.365255.
EndNote Yılmaz İ, Öztürk D (January 1, 2018) Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. Doğal Afetler ve Çevre Dergisi 4 1 34–44.
IEEE İ. Yılmaz and D. Öztürk, “Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”, J Nat Haz Environ, vol. 4, no. 1, pp. 34–44, 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 (January 2018), 34-44. https://doi.org/10.21324/dacd.365255.
JAMA Yılmaz İ, Öztürk D. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. J Nat Haz Environ. 2018;4:34–44.
MLA Yılmaz, İpek and Derya Öztürk. “Integration of Bayesian Networks With GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective”. Doğal Afetler Ve Çevre Dergisi, vol. 4, no. 1, 2018, pp. 34-44, doi:10.21324/dacd.365255.
Vancouver Yılmaz İ, Öztürk D. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective. J Nat Haz Environ. 2018;4(1):34-4.