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Supporting the Forecast of Snow Avalanches in the Canton of Glarus in Eastern Switzerland: A Case Study

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

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Abstract

Snow avalanches pose a serious threat in alpine regions. They may cause significant damage and fatal accidents. Assessing the local avalanche hazard is therefore of vital importance. This assessment is based, amongst others, on daily collected meteorological data as well as expert knowledge concerning avalanche activity. To a data set comprising meteorological and avalanche data collected for the Canton of Glarus in Eastern Switzerland over a period of 40 years, we applied different machine learning strategies aiming at modeling a decision support system in avalanche forecasting.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning, vol. 1. Springer, New York (2006)

    MATH  Google Scholar 

  2. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)

    MATH  Google Scholar 

  3. Bründl, M., Etter, H.J., Steiniger, M., Klingler, C., Rhyner, J., Ammann, W.: IFKIS—a basis for managing avalanche risk in settlements and on roads in Switzerland. Nat. Hazards Earth Syst. Sci. 4(2), 257–262 (2004)

    Article  Google Scholar 

  4. Buser, O., Bütler, M., Good, W.: Avalanche forecast by the nearest neighbour method. Int. Assoc. Hydrol. Sci. 162(2), 557–570 (1987)

    Google Scholar 

  5. Chen, C., Liaw, A., Breiman, L.: Using Random Forest to Learn Imbalanced Data. Technical Report, University of California, Berkeley (2004)

    Google Scholar 

  6. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  7. Gassner, M., Brabec, B.: Nearest neighbour models for local and regional avalanche forecasting. Nat. Hazards Earth Syst. Sci. 2, 247–253 (2002)

    Article  Google Scholar 

  8. Gassner, M., Etter, H.J., Birkeland, K., Leonard, T.: NXD2000: An improved avalanche forecasting program based on the nearest neighbor method. In: ISSW 2000. pp. 52–59. Big Sky, Montana (2000)

    Google Scholar 

  9. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  10. Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT press, Cambridge (2001)

    Google Scholar 

  11. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Series in Statistics, 2nd edn. Springer, New York (2009)

    Book  Google Scholar 

  12. Hendrikx, J., Murphy, M., Onslow, T.: Classification trees as a tool for operational avalanche forecasting on the Seward Highway, Alaska. Cold Reg. Sci. Technol. 97, 113–120 (2014)

    Article  Google Scholar 

  13. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)

    Article  MATH  Google Scholar 

  14. McClung, D., Schaerer, P.A.: The Avalanche Handbook. The Mountaineers Books, Seattle WA (2006)

    Google Scholar 

  15. Mitterer, C., Schweizer, J.: Analyzing the atmosphere-snow energy balance for wet-snow avalanche prediction. In: ISSW 2012. pp. 77–83. Big Sky, Montana (2012)

    Google Scholar 

  16. Möhle, S., Bründl, M., Beierle, C.: Modeling a system for decision support in snow avalanche warning using balanced random forest and weighted random forest. In: Agre, G., Hitzler, P., Krisnadhi, A., Kuznetsov, S. (eds.) Artificial Intelligence: Methodology, Systems, and Applications. LNCS, vol. 8722, pp. 80–91. Springer, Switzerland (2014)

    Google Scholar 

  17. Schweizer, J., Mitterer, C., Stoffel, L.: On forecasting large and infrequent snow avalanches. Cold Reg. Sci. Technol. 59(2), 234–241 (2009)

    Article  Google Scholar 

  18. Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newsl. 6(1), 7–19 (2004)

    Article  Google Scholar 

  19. WSL-Institut für Schnee- und Lawinenforschung SLF, Bundesamt für Umwelt BAFU, Schweizerische Interessengemeinschaft Lawinenwarnsysteme (SILS): Praxishilfe. Arbeit im Lawinendienst: Organisation, Beurteilung lokale Gefährdung und Dokumentation. http://www.slf.ch/dienstleistungen/merkblaetter/praxishilfe_lawdienst_deutsch.pdf

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Acknowledgments

The authors wish to thank the avalanche service of the Canton of Glarus, Switzerland, and the WSL Institute for Snow and Avalanche Research SLF in Davos, Switzerland, for providing the data on which this work is based.

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Authors and Affiliations

  1. International Center for Computational Logic, TU Dresden, Dresden, Germany

    Sibylle Möhle

  2. Department of Computer Science, University of Hagen, Hagen, Germany

    Sibylle Möhle & Christoph Beierle

Authors
  1. Sibylle Möhle
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  2. Christoph Beierle
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Corresponding author

Correspondence to Sibylle Möhle .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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Möhle, S., Beierle, C. (2016). Supporting the Forecast of Snow Avalanches in the Canton of Glarus in Eastern Switzerland: A Case Study. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

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