HIM-STRAT: a neural network-based model for snow cover simulation and avalanche hazard prediction over North-West Himalaya

Abstract

Artificial neural network (ANN)-based models have been developed for simulation of snowpack parameters—RAM hardness, shear strength, temperature, density, thickness and settlement of snowpack layers using manually observed weather data. The simulated snowpack parameters have been used for development of ANN for avalanche prediction. The complete scheme of simulation of snowpack parameters and avalanche prediction has been named as HIM-STRAT and developed for Chowkibal–Tangdhar region in North-West Himalaya using weather and snow stratigraphy data collected at a representative observatory in that region. Weather and snowpack data collected during the period from 1992 to 2016 have been analysed to generate the database of prominent snowpack layers and associated weather variables. Snowpack parameters have been simulated using randomly selected 490 (80%) data points and tested with 123 (20%) data points. Simulated snowpack parameters—RAM hardness, shear strength of the weakest snowpack layer and overburden pressure on the weakest layer—have been used to derive stability index of snowpack (SIS). The SIS and other snowpack parameters such as snowpack height, RAM hardness, shear strength, storm snow and snow temperature have been derived for past 22 winters (1992–2014) and used for prediction of avalanches. The HIM-STRAT has been validated through computation of root-mean-square error of simulated snowpack parameters and accuracy, bias, false alarm rate and Heidke Skill Score of avalanche prediction for five winters from 2014 to 2019. The performance of HIM-STRAT for simulation of snowpack parameters and prediction of avalanches has been found reasonably good and discussed in detail.