Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region

Abstract

Climate change can have adverse effects on various ecosystems on the globe, with the cryosphere being affected to a significant extent. Of the cryosphere, mountain or alpine glaciers are essential resources for freshwater and various ecosystem services. Glacial ablation is the process of removal of snow and ice from a glacier, which includes melting, evaporation, and erosion. The increase in temperature on the Earth due to climate changes is causing rapid glacial abrasion. The rapid global decline in alpine glaciers makes it necessary to identify the key drivers responsible for a glacial retreat to understand the eventual modifications to the surroundings and the Earth’s ecosystem. This study attempts to understand the influence of different driving factors leading to glacier retreat using Machine Learning (ML) and Remote Sensing (RS) techniques. Three models have been developed to estimate the glacial retreat: Feedforward Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The RNN performed the best with an average training and validation accuracy of 0.9. The overall shift of the area estimate has been identified over 10 years. The model thus generated can lead to a better understanding of the region and can provide a baseline for policy and mitigation strategies in the future.