A spatiotemporal deformation modelling method based on geographically and temporally weighted regression

Authors: Yang, Zhijia; Dai, Wujiao; Santerre, Rock; Shi, Qiang
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.
Document Type: Article de recherche
Issue Date: 27 December 2019
Open Access Date: 6 January 2020
Document version: VoR
Permalink: http://hdl.handle.net/20.500.11794/37723
This document was published in: Mathematical problems in engineering, Vol. 29, 1-11 (2019)
Hindawi Publishing Corporation
Alternative version: 10.1155/2019/4352396
Collection:Articles publiés dans des revues avec comité de lecture

Files in this item:
Description SizeFormat 
MathPrEngg Dec 2019 Dai 4352396.pdf2.25 MBAdobe PDFThumbnail
All documents in CorpusUL are protected by Copyright Act of Canada.