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Disasters are unavoidable and natural incidences that happen in the Himalayan regions. They can occur naturally or sometimes also occur due to human intervention. As per the definition, “Disasters are occurrences disrupting the normal existence conditions and causing a level of suffering that is exceeding the capacity of adjustment of the affected community” (Abdulnasir F. Al-Jazairi, 2019). In true sense, when a disaster occurs, regular life becomes unsettled. Also, sometimes disaster can cause a loss of human life. Hence it is essential for public to get information about the encroaching danger so that they can take necessary precautions. One of the disasters that majorly occurs in the hilly and mountainous regions is a snow avalanche. An avalanche is a mass of snow that descend the mountain slopes in a rapid manner due to gravity(Christophe Ancey, 2001). An avalanche occurs when the gravitational force exerted on the snow is more than the resisting forces like the anchoring effect of shrub, etc(Avalanches, 2007). These avalanches consists of ice, rocks, soil or vegetation(Jurg Schweizer et al., 2003).
Forecasting of snow avalanche is required to safeguard a human life. Avalanche forecasting is defined as “the prediction of current and future snow instability in space and time relative to a given triggering level”(D M McClung, 2002a). The stability of the snow cover also depends on fixed parameters like the path of the avalanche, mean slope, etc. or variable parameters like the weather conditions such as amount of new snow, water content, wind, etc.(Christophe Ancey, 2001). However, ranking of the different single data elements may be done depending on the relevance and the ease with which one can interpret the instability of the snow. This is known as informational entropy(D M McClung, 2002b). Data was classified in to three classes based on the entropy. Class I consisted of stability factors of snowpack. Class II consisted of snowpack factors depicting the structure of snowpack required for avalanches. According to it, the highest entropy was observed in the data present in Class III which included the snow and weather data that was measured near or at the surface. This data has been majorly used in avalanche forecasting and various techniques incorporating the use of this snow and meteorological data have been proposed for forecasting(Amreek Singh & Ashwagosha Ganju, 2004; Martin Gassner et al., 2000, p. 200; Othmar Buser, 1983).
India is the part of the Himalayan range- snow clad region. Because of this, India has a history of avalanches, as avalanches have been observed in the Himachal Pradesh, Jammu and Kashmir, Uttaranchal and Sikkim(Amreek Singh et al., 2004; Evgeny Andreevich Podolskiy et al., 2009). In India, the avalanche occurrence information is obtained from bulletins(Joshi et al., 2020). Other than that, India has also implemented avalanche forecasting models to obtain the status of avalanche occurrence(Amreek Singh et al., 2015, 2017; Amreek Singh & Ashwagosha Ganju, 2004). Forecasting of avalanche was proposed using nearest neighbour algorithm(Charles Obled & Walter Good, 1980). Models have been implemented based on nearest neighbour for avalanche forecasting around the globe(Martin Gassner et al., 2000; Othmar Buser, 1983). Similarly, eeN10, avalanche forecasting model based on nearest neighbour has already been implemented in India to deal with this issue(Amreek Singh et al., 2015, 2017).