The anomalous movements of glaciers cause disasters,such as debrisflows and landslides.It is very important to assess the glacier movements and their future trends.Glacier velocity refers to movement process.The curre...The anomalous movements of glaciers cause disasters,such as debrisflows 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 thefluctuation 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 withfluctuations;that is,they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term.展开更多
基金supported by the National Scientific Foundation of China[grant number 42161063]Open Foundation of Key Laboratory of Yellow River Water Environment in Gansu Province[grant number 121YRWEK001]+4 种基金Science and Technology Plan of Gansu Province[grant number 20JR2RA002]Natural Science Foundation of Gansu Province[grant number 20JR10RA249]Youth Science and Technology Foundation of Gansu Province[grant number 20JR10RA272]Lanzhou Jiaotong University-Tianjin University Innovation Project Fund Project[grant number 2020055]Jiayuguan City 2021 Science and Technology Plan Projects[grant number 21-35].
文摘The anomalous movements of glaciers cause disasters,such as debrisflows 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 thefluctuation 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 withfluctuations;that is,they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term.
