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

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.