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

Yang, Zheng; Xi, Wenfei; Yang, Zhiquan; Shi, Zhenguo; Qian, Tanghui

doi:10.3390/s22218350

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…However, the predictive accuracy of these models is often unsatisfactory, and the accuracy needs to be maintained through a rolling forecast. Yang Zhengrong 7 used the glacier deformation field and gray correlation analysis obtained by InSAR technology and revealed the relationship between glacier deformation and various factors such as temperature, precipitation, and slope. Xiong Zhiqiang 8 used the statistical properties of curve fitting to select the best curve model from several candidate curve models to predict the subsidence time series.…”

Section: Introductionmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

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Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.

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Section: Introductionmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

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“…In addition, the actual surface subsidence is different under different geological and mining conditions, but the prediction result is the same if the same time function is used, which is contradictory to the actual situation. Therefore, researchers have focused on "late models", such as the grey model [31], regression analysis [32,33], support vector machine regression [9], Bayesian network [10], wavelet analysis [34], and artificial neural network [35,36]. These models rely on modern and efficient monitoring means such as GNSS, InSAR, and LIDAR to obtain long-term series monitoring data and analyze internal statistical laws and trends.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mine Subsidence Based on InSAR Technology and the LSTM Algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China)

Sui

Lian

2023

Remote Sensing

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The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this study proposes an approach to predicting mine subsidence that leverages Interferometric Synthetic Aperture Radar (InSAR) technology and the long short-term memory network (LSTM). The proposed approach utilizes small baseline multiple-master high-coherent target (SBMHCT) interferometric synthetic aperture radar technology to monitor the mine surface and applies the long short-term memory (LSTM) algorithm to construct the prediction model. The Shigouyi coalfield in Ningxia Province, China was chosen as a study area, and time series ground subsidence data were obtained based on Sentinel-1A data from 9 March 2015 to 7 June 2016. To evaluate the proposed approach, the prediction accuracies of LSTM and Support Vector Regression (SVR) were compared. The results show that the proposed approach could accurately predict mine subsidence, with maximum absolute errors of less than 2 cm and maximum relative errors of less than 6%. The findings demonstrate that combining InSAR technology with the LSTM algorithm is an effective and robust approach for predicting mine subsidence.

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“…Mining-induced surface deformation will cause geological disasters, such as collapse pits, cracks, and subsided steps on the surface, and damage to surface structures, water bodies, and railways, seriously affecting residents' normal production and life in mining areas. Therefore, real-time and highprecision monitoring, prediction, and early warning of surface disasters are significant for safe and efficient production and environmental protection in mining areas Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Study on large-gradient deformation of mining areas based on InSAR-PEK technology

Tan,

Yu,

Zhu

et al. 2023

Earth sci. res. j.

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To solve large-gradient deformation in mining areas unavailable by SAR data, a method combining PIM Exponent Knothe (PEK) model and InSAR technology (InSAR-PEK) was proposed to predict the mining-induced subsidence and obtain the large-gradient deformation dynamically. Firstly, the maximum subsidence value predicted by the probability integration method was combined with SAR data, and the subsidence values in the initial and residual periods were obtained. Secondly, three groups of power exponent Knothe function parameters were obtained, including csar and ksar based on SAR data, clevel_wz, and klevel_wz based on leveling data over a complete observation period, and clevel_bf and klevel_bf based on the elimination of the leveling data in the main period. Finally, the predicted values of the three groups of parameters were compared with the measured data, respectively, and the root mean square errors (RMSE) were obtained. The engineering example verified that RMSEs were 28.1mm~91.7mm in the main period and 30.9mm~58.7mm in the whole period estimated by the InSAR-PEK method. The results showed that the subsidence values in the main period were relatively stable by the InSAR-PEK method, and some points' prediction accuracy was better than that of leveling data. The predicted values obtained by the InSAR-PEK method and those extracted by SAR were compared with the measured values. In the main period, the values extracted by SAR differed greatly from the measured values, which were false values. However, the predicted values by the InSAR method were close to the measured values, which can be used to independently get subsidence values in the main period from SAR data.

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

Cited by 7 publications

References 49 publications

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Prediction of Mine Subsidence Based on InSAR Technology and the LSTM Algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China)

Study on large-gradient deformation of mining areas based on InSAR-PEK technology

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