Development and Comparison of InSAR-Based Land Subsidence Prediction Models
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| Publikašuvnnas: | Remote Sensing vol. 16, no. 17 (2024), p. 3345 |
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| Váldodahkki: | |
| Eará dahkkit: | , , , , , |
| Almmustuhtton: |
MDPI AG
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs16173345 |2 doi | |
| 035 | |a 3104053502 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Zheng, Lianjing |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 245 | 1 | |a Development and Comparison of InSAR-Based Land Subsidence Prediction Models | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction. | |
| 653 | |a Geology | ||
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Mathematical models | ||
| 653 | |a Multilayers | ||
| 653 | |a Deformation | ||
| 653 | |a Regression analysis | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Mathematical functions | ||
| 653 | |a Deformation effects | ||
| 653 | |a Deformation analysis | ||
| 653 | |a Subsidence | ||
| 653 | |a Machine learning | ||
| 653 | |a Engineering geology | ||
| 653 | |a Heat conductivity | ||
| 653 | |a Prediction models | ||
| 653 | |a Aquifers | ||
| 653 | |a Interferometric synthetic aperture radar | ||
| 653 | |a Data analysis | ||
| 653 | |a Land subsidence | ||
| 653 | |a Precipitation | ||
| 653 | |a Drought | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Support vector machines | ||
| 653 | |a Groundwater | ||
| 653 | |a Interferometry | ||
| 653 | |a Time series | ||
| 653 | |a Algorithms | ||
| 653 | |a Human engineering | ||
| 653 | |a Predictions | ||
| 700 | 1 | |a Wang, Qing |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 700 | 1 | |a Cao, Chen |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 700 | 1 | |a Shan, Bo |u China Power Engineering Consulting Group, Northeast Electric Power Design Institute Co., Ltd., Changchun 130021, China; <email>shanbo@nepdi.net</email> | |
| 700 | 1 | |a Jin, Tie |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 700 | 1 | |a Zhu, Kuanxing |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 700 | 1 | |a Li, Zongzheng |u College of Construction Engineering, Jilin University, Changchun 130022, China; <email>zhenglj20@mails.jlu.edu.cn</email> (L.Z.); <email>wangqing@jlu.edu.cn</email> (Q.W.); <email>jintie23@mails.jlu.edu.cn</email> (T.J.); <email>zhukx21@mails.jlu.edu.cn</email> (K.Z.); <email>zongzheng21@mails.jlu.edu.cn</email> (Z.L.) | |
| 773 | 0 | |t Remote Sensing |g vol. 16, no. 17 (2024), p. 3345 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
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