Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Zhengrong Yang; Wenfei Xi; Zhiquan Yang; Zhengtao Shi; Tanghui Qian

doi:10.3390/s22218350

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Sensors (Basel). 2022 Oct 31;22(21):8350. doi: 10.3390/s22218350.

Authors

Zhengrong Yang¹, Wenfei Xi^{1

2}, Zhiquan Yang^{2

3

4}, Zhengtao Shi¹, Tanghui Qian¹

Affiliations

¹ Faculty of Geography, Yunnan Normal University, Kunming 650500, China.
² Key Laboratory of Early Rapid Identification, Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province, Kunming 650093, China.
³ Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming 650093, China.
⁴ Key Laboratory of Geological Disaster Risk Prevention and Control and Emergency Disaster Reduction of Ministry of Emergency Management of the People's Republic of China, Kunming University of Science and Technology, Kunming 650093, China.

Abstract

The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is -212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north-south direction and east-west direction is -125.63 mm, -77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between -94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R², MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

Keywords: GA-BP; InSAR technology; glacier deformation; monitoring and prediction; mountain glaciers.

MeSH terms

Climate Change
Ice Cover*
Neural Networks, Computer
Snow*
Technology

Grants and funding

This research was funded by the National Natural Science Foundation of China (Grant No. 41861134008), Muhammad Asif Khan academician workstation of Yunnan Province (Grant No. 202105AF150076), General program of Yunnan Province Science and Technology Department (Grant No. 202105AF150076), Key Project of Natural Science Foundation of Yunnan Province (Grant No. 202101AS070019), Key R&D Program of Yunnan Province (Grant No. 202003AC100002), and General Program of basic research plan of Yunnan Province (Grant No. 202001AT070043).