Abstract
ABSTRACT The anomalous movements of glaciers cause disasters, such as debris flows and landslides. It is very important to assess the glacier movements and their future trends. Glacier velocity refers to movement process. The current research aims to analyse past and current spatiotemporal changes in glacier velocity. No study has used neural network model to conduct a spatiotemporal prediction for glacier velocity. Therefore, this paper selected typical mountain glaciers G2 and G5 along the Sichuan-Tibet Railway as research objects and constructed the Convolutional Gate Recurrent Unit (ConvGRU) spatiotemporal prediction model based on 1988–2018 Landsat data to predict velocities in 2019–2028, and analysed the future trends of G2 and G5. The evaluation indexes met the model requirements to a large extent, quantitatively showing that the model has high accuracy and can successfully capture the fluctuation changes in time series data of glacier velocity. The mean deviations of G2 and G5 were 0.09 and −0.47 m/yr, respectively, reflecting the high reliability of the model applied to extraction of glacier velocity. The velocities of G2 and G5 showed a slow downtrend with fluctuations; that is, they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term.
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