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Development and Comparison of InSAR-Based Land Subsidence Prediction Models

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Pubblicato in:Remote Sensing vol. 16, no. 17 (2024), p. 3345
Autore principale: Zheng, Lianjing
Altri autori: Wang, Qing, Cao, Chen, Shan, Bo, Jin, Tie, Zhu, Kuanxing, Li, Zongzheng
Pubblicazione:
MDPI AG
Soggetti:
Geology
Accuracy
Data processing
Mathematical models
Multilayers
Deformation
Regression analysis
Multilayer perceptrons
Mathematical functions
Deformation effects
Deformation analysis
Subsidence
Machine learning
Engineering geology
Heat conductivity
Prediction models
Aquifers
Interferometric synthetic aperture radar
Data analysis
Land subsidence
Precipitation
Drought
Neural networks
Artificial intelligence
Support vector machines
Groundwater
Interferometry
Time series
Algorithms
Human engineering
Predictions
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Abstract: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.
ISSN:2072-4292
DOI:10.3390/rs16173345
Fonte:Advanced Technologies & Aerospace Database