International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 193–198, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-193-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 193–198, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-193-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

MULTI-FACTOR BUILDING DEFORMATION ANALYSIS AND PREDICTION MODEL BASED ON MULTI-SOURCE DATE

M. Q. Huang1, L. Zhou1,3, L. X. Qi1, H. Y. Huang1, M. Y. Tang2, and Y. J. Shi2 M. Q. Huang et al.
  • 1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
  • 2Guangxi Deep Space Sail Engineering Survey and Design Co., LTD., Nanning 530022, China
  • 3Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, China

Keywords: deformation analysis, Kalman filter, GM(1,1), Grey relational analysis, hybrid model

Abstract. Normally, when using standard Kalman filter to analyze and predict the buildings deformation, the influence of a single factor is generally considered, or some factors are selected subjectively. In many cases, the objective influence of multiple effective factors on the model cannot be really considered, which adversely affects the accuracy of the model prediction and then affects the adaptability and prediction accuracy of the model. Aim at this problem, in this paper, we introduced grey relational analysis to determine the factor choice by calculating the grey relational grade of each impact factor. Then, we regarded the selected factors as state input vectors. Finally, we incorporated the state input vectors into model to establish grey relational Kalman filter model with considering multi-factors. In addition, we compared and analyzed the grey relational Kalman filter model with the stepwise regression model and BP neural network model that both can take into consideration the influence of multiple factors. The result of example analysis shows that the grey relational Kalman filter model can effectively select the factor which has great influence on deformation into the model as the state input vector during the modeling process, and the prediction accuracy of the recursive algorithm of standard Kalman filter is improved. Compared with the stepwise regression model and BP neural network model, the self-adaptability of the grey relational Kalman filter model is improved and the accuracy of the prediction results is also higher.