IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v36y2017i2p165-180.html
   My bibliography  Save this article

Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees

Author

Listed:
  • Gail Blattenberger
  • Richard Fowles

Abstract

Little Cottonwood Canyon Highway is a dead‐end, two‐lane road leading to Utah's Alta and Snowbird ski resorts. It is the only road access to these resorts and is heavily traveled during the ski season. Professional avalanche forecasters monitor this road throughout the ski season in order to make road closure decisions in the face of avalanche danger. Forecasters at the Utah Department of Transportation (UDOT) avalanche guard station at Alta have maintained an extensive daily winter database on explanatory variables relating to avalanche prediction. Whether or not an avalanche crosses the road is modeled in this paper via Bayesian additive tree methods. Utilizing daily winter data from 1995 to 2011, results show that using Bayesian tree analysis outperforms traditional statistical methods in terms of realized misclassification costs that take into consideration asymmetric losses arising from two types of error. Closing the road when an avalanche does not occur is an error harmful to resort owners, and not closing the road when one does may result in injury or death. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Gail Blattenberger & Richard Fowles, 2017. "Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 165-180, March.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:2:p:165-180
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yakun Wang & Zeda Li & Scott A. Bruce, 2023. "Adaptive Bayesian sum of trees model for covariate‐dependent spectral analysis," Biometrics, The International Biometric Society, vol. 79(3), pages 1826-1839, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:36:y:2017:i:2:p:165-180. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.