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Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China

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A Correction to this article was published on 16 July 2020

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Abstract

Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literature, there are only a few methods that can effectively monitor GD. Microwave remote sensing data such as interferometric synthetic aperture radar (InSAR) are mostly adopted to assess GD. These data can reveal the surface deforming areas with great precision, mapping GD results at a large scale. In this study, the effects of GD and the influencing factors, such as the building area, the water level, the cumulative precipitation, and the cumulative temperature, are modeled in the Erhai region with small baseline subset interferometric SAR (SBAS-InSAR) data that are applied using machine learning (ML) methods. The ML methods, namely, multiple linear regression (MLR), multilayer perceptron backpropagation (MLP-BP), least squares support vector machine (LSSVM), and particle swarm optimization (PSO)-LSSVM, are used to predict GD, and the results are compared. Particularly, the PSO-LSSVM method has obtained the least root mean square error (RMSE) and mean relative error (MRE) of 11.448 and 0.112, respectively. Therefore, the results have proven that the proposed PSO-LSSVM is very efficient in analyzing GD.

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  • 16 July 2020

    Unfortunately, the name of the corresponding author (Wenxiang Wu) was missing in the author group section of the published paper.

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Funding

This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040101, XDA19040304).

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Authors and Affiliations

  1. Academician Workstation of Zhai Mingguo, University of Sanya, Sanya, 572000, China

    Yahui Guo & Wenxiang Wu

  2. Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwaidajie 19, Beijing, 100875, China

    Yahui Guo

  3. College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China

    Shunqiang Hu & Yuyi Wang

  4. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, 100101, China

    Wenxiang Wu

  5. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, Singapore

    J. Senthilnath

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For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “conceptualization, YH.G.; methodology, YH.G., J.S., WX.W.; software, SQ.H., WX.W.; validation, YH.G.; formal analysis, YH.G.; investigation, YH.G., WX.W.; resources, YH.G.; data curation, YH.G.; writing—original draft preparation, YH.G.; writing—review and editing, YH.G., J.S; visualization, YH.G.; supervision, YH.G.; funding acquisition, YH.G., YY.W., please turn to the CRediT taxonomy for the term explanation. Authorship must be limited to those who have contributed substantially to the work reported.

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Correspondence to Wenxiang Wu.

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Guo, Y., Hu, S., Wu, W. et al. Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China. Environ Monit Assess 192, 464 (2020). https://doi.org/10.1007/s10661-020-08426-8

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