International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Nearest neighbour classification model in avalanche prediction- a review

Author Affiliations

  • 1P.G. Department of Computer Science, S.N.D.T. Women’s University, Mumbai-400049, India
  • 2P.G. Department of Computer Science, S.N.D.T. Women’s University, Mumbai-400049, India

Int. Res. J. Earth Sci., Volume 5, Issue (9), Pages 18-23, October,25 (2017)

Abstract

Prediction of snow avalanche has come a long way since its inception. Nearest neighbour algorithm which mainly incorporates the use of snow-meteorological variables as input for finding the probability of avalanche occurrence, forms an integral part in these prediction algorithms. These methods search the historical data to find days with similar conditions and events that are associated with them to formulate a forecast for the current day. The early methods that began by using simple implementation of nearest neighbour have now been modified so as to find a more accurate forecasting model. In this paper, we present the study of the nearest neighbour classification models that have been used for the prediction of snow avalanche.

References

  1. Schweizer J., Bruce Jamieson J. and Schneebeli M. (2003)., Snow avalanche formation., Reviews of Geophysics, 41(4). doi:10.1029/2002RG000123
  2. Schweizer J., Bartelt P. and van Herwijnen A. (2015)., Snow Avalanches., In Snow and Ice-Related Hazards, Risks and Disasters, 395-436.
  3. Jurg S. and Paul M.B.F. (1996)., Avalanche Forecasting-An Expert System Approach., Journal Of Glaciology, 42(141), 318-332.
  4. Buser O. (1983)., Avalanche forecast with the method of nearest neighbours: an interactive approach., Cold Regions Science and Technology, 8(2), 155-163.
  5. Pozdnoukhov A., Matasci G., Kanevski M. and Purves R.S. (2011)., Spatio-temporal avalanche forecasting with Support Vector Machines., Natural Hazards and Earth System Science, 11(2), 367-382. doi:10.5194/nhess-11-367-2011
  6. Obled C. and Good W. (1980)., Recent Developments of Avalanche Forecasting by Discriminant Analysis Techniques: A Methodological Review and Some Applications to the Parsenn Area (Davos, Switzerland)., Journal Of Glaciology, 25(92), 315-346.
  7. Buser O. and Good W. (1984)., Avalanche forecast: experience using nearest neighbours., Int. Snow Science Workshop, Aspen, Col, 24-27.
  8. Othmar B., Monika B. and Walter G. (1987)., Avalanche forecast by the nearest neighbour method., International Association of Hydrological Sciences Publication, 162, 557-569.
  9. Bolognesi R., Buser O. and Good W. (1994)., Local avalanche forecasting in Switzerland: strategy and tools, a new approach., International Snow Science Workshop, 463-472.
  10. Kristensen K. and Larsson C. (1994)., An avalanche forecasting program based on a modified nearest neighbour method., International Snow Science Workshop, 22-30.
  11. Gassner M., Etter H.J., Birkeland K. and Leonard T. (2000)., NXD2000: An improved avalanche forecasting program based on the nearest neighbor method., International Snow Science Workshop, 52-59.
  12. Bernhard B. and Roland M. (2001)., A Nearest Neighbor Model For Regional Avalanche Forecasting., Annals Of Glaciology, 32, 130-134.
  13. Mérindol L., Guyomarc’h G. and Giraud G. (2002)., A French local tool for avalanche hazard forecasting: Astral, current state and new developments., In Proceedings of the International Snow Science Workshop, Pentiction, Canada, 105-108.
  14. Purves R., Morrison K., Moss G. and Wright B. (2002)., Cornice—development of a nearest neighbors model applied in backcountry avalanche forecasting in Scotland., In Proceedings of International Snow Science Workshop, 117-122.
  15. Purves R.S., Morrison K.W., Moss G. and Wright D.S.B. (2003)., Nearest neighbours for avalanche forecasting in Scotland—development, verification and optimisation of a model., Cold Regions Science and Technology, 37(3), 343-355. doi:10.1016/S0165-232X(03)00075-2
  16. McCollister C., Birkeland K., Hansen K., Aspinall R. and Comey R. (2002)., A probabilistic technique for exploring multi-scale spatial patterns in historical avalanche data by combining GIS and meteorological nearest neighbors with an example from the Jackson Hole Ski Area, Wyoming., Proceedings of the 2002 International Snow Science Workshop.
  17. McCollister C., Birkeland K., Hansen K., Aspinall R. and Comey R. (2003)., Exploring multi-scale spatial patterns in historical avalanche data, Jackson Hole Mountain Resort, Wyoming., Cold Regions Science and Technology, 37(3), 299-313. doi:10.1016/S0165-232X(03)00072-7
  18. Singh A. and Ganju A. (2004)., A supplement to nearest-neighbour method for avalanche forecasting., Cold Regions Science and Technology, 39(2-3), 105-113. doi:10.1016/j.coldregions.2004.03.005
  19. Singh A., Srinivasan K. and Ganju A. (2005)., Avalanche Forecast Using Numerical Weather Prediction In Indian Himalaya., Cold Regions Science and Technology, 43(1-2), 83-92. doi:10.1016/j.coldregions.2005.05.009
  20. Singh A. and Ganju A. (2008)., Artificial Neural Networks for Snow Avalanche Forecasting in Indian Himalaya., In Proceedings of 12th International Conference of International Association for Computer Methods and Advances in Geomechanics, IACMAG, 16.
  21. Cordy P., McClung D.M., Hawkins C.J., Tweedy J. and Weick T. (2009)., Computer assisted avalanche prediction using electronic weather sensor data., Cold Regions Science and Technology, 59(2-3), 227-233. doi:10.1016/j.coldregions.2009.07.006
  22. Chandra B., Singh A., Singh D. and Ganju A. (2010)., Features ranking for avalanche forecasting: method and results for north-western Himalaya., International Snow Science workshop, 196-203.
  23. Singh A., Damir B., Deep K. and Ganju A. (2015)., Calibration of nearest neighbors model for avalanche forecasting., Cold Regions Science and Technology, 109, 33-42. doi:10.1016/j.coldregions.2014.09.009
  24. Singh A., Deep K. and Grover P. (2017)., A novel approach to accelerate calibration process of a k -nearest neighbours classifier using GPU., Journal of Parallel and Distributed Computing, 104, 114-129. doi:10.1016/j.jpdc.2017.01.003