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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2019-379
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-379
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Nov 2019

Submitted as: research article | 26 Nov 2019

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A revised version of this preprint is currently under review for the journal NHESS.

Data efficient Random Forest model for avalanche forecasting

Manesh Chawla1 and Amreek Singh2 Manesh Chawla and Amreek Singh
  • 1Snow and Avalanche Study Establishment, Manali - 175103, India
  • 2Snow and Avalanche Study Establishment, Chandigarh - 160037, India

Abstract. Fast downslope release of snow (avalanche) is a serious hazard to people living in snow bound mountains. Released snow mass can gain sufficient momentum on its down slope path to kill humans, uproot trees and rocks, destroy buildings. Direct reduction of avalanche threat is done by building control structures to add mechanical support to snowpack and reduce or deflect downward avalanche flow. On large terrains it is economically infeasible to use these methods on each high risk site.Therefore predicting and avoiding avalanches is the only feasible method to reduce threat but sufficient snow stability data for accurate forecasting is generally unavailable and difficult to collect. Forecasters infer snow stability from their knowledge of local weather, terrain and sparsely available snowpack observations. This inference process is vulnerable to human bias therefore machine learning models are used to find patterns from past data and generate helpful outputs to minimise and quantify uncertainty in forecasting process. These machine learning techniques require long past records of avalanches which are difficult to obtain. In this paper we propose a data efficient Random Forest model to address this problem. The model can generate a descriptive forecast showing reasoning and patterns which are difficult to observe manually. Our model advances the field by being inexpensive and convenient for operational forecasting due to its data efficiency, ease of automation and ability to describe its decisions.

Manesh Chawla and Amreek Singh

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Manesh Chawla and Amreek Singh

Manesh Chawla and Amreek Singh

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