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A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

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  • Zhijia Yang
  • Wujiao Dai
  • Rock Santerre
  • Cuilin Kuang
  • Qiang Shi

Abstract

The geographically and temporally weighted regression (GTWR) model is a dynamic model which considers the spatiotemporal correlation and the spatiotemporal nonstationarity. Taking into account these advantages, we proposed a spatiotemporal deformation modelling method based on GTWR. In order to further improve the modelling accuracy and efficiency and considering the application characteristics of deformation modelling, the inverse window transformation method is used to search the optimal fitting window width and furthermore the local linear estimation method is used in the fitting coefficient function. Moreover, a comprehensive model for the statistical tests method is proposed in GTWR. The results of a dam deformation modelling application show that the GTWR model can establish a unified spatiotemporal model which can represent the whole deformation trend of the dam and furthermore can predict the deformation of any point in time and space, with stronger flexibility and applicability. Finally, the GTWR model improves the overall temporal prediction accuracy by 43.6% compared to the single-point time-weighted regression (TWR) model.

Suggested Citation

  • Zhijia Yang & Wujiao Dai & Rock Santerre & Cuilin Kuang & Qiang Shi, 2019. "A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, December.
    • Handle: RePEc:hin:jnlmpe:4352396
    DOI: 10.1155/2019/4352396
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