Modelling glacial retreat using Machine learning Techniques
The effects of climate change and global warming have modified the earth’s ecosystem extensively. Glacier and polar ice caps are some of the worst effected regions. The reduction of permanent ice or retreat of glacial ice is of a big concern. The permanent change to these glaciers in the long run shall modify the earth’s ecosystem extensively. The effects due to these external and other meteorological factors can have adverse and dire effects if not mitigated properly. Understanding the modifications of these glaciers and the rate of ice retreat can provide a long understanding for the regions of concern. The current attempts to understand and simulate the overall change within the glacier retreat within a stipulated time frame and develop machine learning based techniques, which can predict over a long run based on surrounding and influencing factors. The model thus generated, can lead to a better understanding of the region as well as provide a keen insight on the parameters of concern.
