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A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya

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Abstract

Avalanche forecasting is carried out using physical as well as statistical models. All these models have certain limitations associated with their mathematical formulation that enable them to perform variably with respect to forecast of an avalanche event and associated danger. To overcome limitations of each individual model, a multi-model decision support system (MM-DSS) has been developed for forecasting of avalanche danger in Chowkibal–Tangdhar (C-T) region of North-West Himalaya. The MM-DSS has been developed for two different altitude zones of the C-T region by integrating four avalanche forecasting models-Hidden Markov model (HMM), nearest neighbour (NN), artificial neural network (ANN) and snow cover model-HIM-STRAT to deliver avalanche forecast with a lead time of three days. Weather variables for these models have been predicted using ANN. Root mean square error of predicted weather variables is computed by using leave one out cross-validation method. Snow and meteorological data of 22 winters (1992–2014) of the lower C-T region and 8 winters (2008–2016) of the higher C-T region have been used to develop avalanche forecasting models for these two sub-regions. All the avalanche forecasting models have been validated by true skill score (TSS), Heidke skill score (HSS), per cent correct (PC), probability of detection (POD), bias and false alarm rate (FAR) using data of five winters (2014–19) for the lower C-T region and three winters (2016–19) for the upper C-T region. In both the C-T regions, for day-1, day-2 and day-3, the HSS of MM-DSS lies between 0.26 and 0.4 and the POD between 0.64 and 0.86.

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Acknowledgements

The authors acknowledge Director SASE for his approval to initiate this work under DRDO-funded project—Him Sandesh. The authors also acknowledge reviewers of this paper for valuable suggestions and positive criticism. Dr. SB Roy, Department of Atmospheric Science, IIT Delhi, is duly acknowledged for generating WRF data for project—Him Sandesh. The technical staff of SASE is also acknowledged for their valuable contribution in the collection of manual field data.

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  1. Snow and Avalanche Study Establishment, Chandigarh, India

    Prabhjot Kaur & Jagdish Chandra Joshi

  2. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Preeti Aggarwal

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  1. Prabhjot Kaur
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  2. Jagdish Chandra Joshi
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  3. Preeti Aggarwal
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Correspondence to Jagdish Chandra Joshi.

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Kaur, P., Joshi, J.C. & Aggarwal, P. A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya. Nat Hazards 110, 563–585 (2022). https://doi.org/10.1007/s11069-021-04958-5

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