Abstract
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal–Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992–2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
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Acknowledgements
The authors acknowledge all data collection teams of SASE for their precious contribution in the collection of valuable snow and meteorological data in the harsh climatic conditions of the Himalayas. Authors would like to give due credit to Sh. Dan Singh who helped in the comparative study of the HMM with the NN model. They also acknowledge their colleagues for valuable technical suggestions and critical comments.
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Joshi, J.C., Srivastava, S. A Hidden Markov Model for avalanche forecasting on Chowkibal–Tangdhar road axis in Indian Himalayas. J Earth Syst Sci 123, 1771–1779 (2014). https://doi.org/10.1007/s12040-014-0510-4
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DOI: https://doi.org/10.1007/s12040-014-0510-4