Modelling of Gangotri glacier thickness and volume using an artificial neural network

The volume of glaciers in a glacierized basin is an important characteristic for the existence of the glaciers and their evolution. Knowledge of glacier volume motivates scientific interest for two main reasons. First, the volumes of individual glaciers are monitored to estimate future water and sea level rises. Second, glaciers in the Indian Himalayas have been recognized as important water storage systems for municipal, industrial, and hydroelectric power generation purposes. Therefore, estimation of glacier volume is desired to estimate sea level rise accurately. The problem of deriving volume and glacier ice thickness is solved by developing an artificial neural network (ANN) approach that requires glacier boundaries, central branch lines, width-wise lines, digital elevation model (DEM), and slope information. Two geomorphic assumptions were taken in this investigation after testing, and strong relationships were found between elevation values of the frontal ice-denuded area of the Gangotri glacier and ice thickness derived from an ANN.