Geodetic deformation forecasting based on multi-variable grey prediction model and regression model

Abstract

Purpose

The purpose of this paper is to examine the effectiveness of the multivariable grey prediction model in deformation forecasting.

Design/methodology/approach

Deformation in a dam can be seen because of many factors but without any doubt, the most influential factor is the water level. In this study, the deformation level of a point in the Keban Dam crest has been tried to be forecasted depending on the water level by the multivariable grey model GM(1,N). Regression analysis was used to test the accuracy of the prediction results obtained using the grey prediction model.

Findings

The results show that there is a great consistency between the grey prediction values and the actual values, and that the GM(1,N) produces more reliable results than the regression analysis. Based on the results, it can be concluded that the GM(1,N) is a very reliable estimation model for limited data conditions.

Originality/value

Different from the other studies in the literature, this study investigates deformation in a dam subject to the water level in the dam reservoir. The main contribution of the study to the literature is to suggest a relatively new procedure for estimating the deformation in the dams based on the water level.

Citation

Kose, E. and Tasci, L. (2019), "Geodetic deformation forecasting based on multi-variable grey prediction model and regression model", Grey Systems: Theory and Application, Vol. 9 No. 4, pp. 464-471. https://doi.org/10.1108/GS-04-2019-0007