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

机译:基于地理和临时加权回归的时空变形建模方法

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摘要

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.
机译:地理上和时间加权回归(GTWR)模型是一种动态模型,其考虑了时空相关性和时空非间抗性。考虑到这些优势,我们提出了一种基于GTWR的时空变形建模方法。为了进一步提高建模精度和效率并考虑变形建模的应用特性,逆窗变换方法用于搜索最佳拟合窗口宽度,此外,在拟合系数函数中使用局部线性估计方法。此外,在GTWR中提出了统计测试方法的综合模型。坝变形建模应用的结果表明,GTWR模型可以建立一个统一的时空模型,可以代表大坝的整个变形趋势,此外可以预测任何时间点和空间的变形,具有较强的灵活性和适用性。最后,与单点时间加权回归(TWR)模型相比,GTWR模型将整体时间预测精度提高了43.6%。

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