Prediction of Tidal Effect in Crustal Deformation Monitoring: A Geodetic Perspective

Yakubu Issaka, Yao Yevenyo Ziggah, Kenneth Antwi Baafi

Abstract


The gravitational force that arises by the pull from external objects varies from one part of the affected object to the other. These differential pulls produce what are known as tidal forces. This tidal force leads to deformation of the earth crust which can be in both the horizontal and vertical planes. It is quite agreeable that over the years there have been growing interest in crustal deformation monitoring and many geodetic techniques such as precise levelling measurements, angle and distance measurements, photogrammetric and Global Positioning System (GPS) have been adopted for this purpose. However, literature has shown that tidal effects have not been given much consideration in crustal deformation and structural health monitoring studies. In the present study, three mathematical approaches namely Auto-Regressive Integrated Moving Average (ARIMA) Time Series, Non-Linear Auto-Regressive Neural Network (NARNET), and the Hybrid ARIMA and Neural Network model have been used  to model and predict the tidal effect on the earth crust for geodetic deformation monitoring These models were developed from a set of quantified deformation values based on geographic locations of points found in five regions of Ghana on which a prediction of future trend was made.  The average deformation values produced by ARIMA, NARNET and Hybrid model are 0. 002004, 0.001900, and 0.00242 m, respectively. The ARIMA, NARNET and hybrid models showed average over- and under-estimation values of 0.000033, 0.000310 and -0.00046 m corresponding to their mean residuals.   In assessing the precision of the estimated tidal values, the Hybrid model performed slightly better than the NARNET and the ARIMA models with average standard deviation of 0.00039, 0.000780 and 0.000784 m respectively. As such, future tidal deformation values can best be predicted in deformation monitoring assessment by using the Hybrid ARIMA and Neural Network models.

Keywords


Tides; Auto-Regressive Integrated Moving Average; Time Series; Non-Linear Auto-Regressive Neural Network; Crustal Deformation

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References


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