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This study aims to use GPS/Levelling data and machine learning techniques (MLs) to model a high precision local geoid for Kuwait. To improve the accuracy of a local geoid the global geopotential model and local terrain effect should be incorporated. The geoid model was improved based on the modelling of geoid residuals using three MLs. Minimax Probability Machine Regression (MPMR), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS) MLs were developed for modelling the calculated geoid residuals. The results show that the accuracy of the three MLs was improved compared to previous studies, and the accuracy of the GPR model was better than the other models. The standard deviations of Kuwait geoid undulation determined by GPS/Levelling, gravimetric, and developed GPR models were 1.377, 1.375, 1.375 m, respectively. Thus, the developed GPR model has successfully predicted an accurate geoid height of Kuwait with maximum variation approaches ±0.02 m. [ABSTRACT FROM AUTHOR]

This study investigates to use GPS/Levelling measurements of Kuwait and four heuristic regression methods including Least Square Support Vector Regression (LSSVR), Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Multivariate Adaptive Regression Splines (MARS) for modelling local geoid undulation. The accuracy of the models was compared by geoid undulation of gravitational observations and Global Geopotential Models (GGMs). The results show that the KRR model is suitable for Kuwait geoid model, its error of percentage is 0.018 and 0.124% relative to gravity and GPS/Levelling geoid undulation models, respectively. Furthermore, the comparison of KRR model with GGMs models signifies its accuracy. [ABSTRACT FROM AUTHOR]

This study aims at the investigation of GNSS-PP method to determine the dynamic characteristics of structures. Cantilever steel bars having lengths of 70, 100 and 120 cm were tested under dynamic excitation. The GNSS was used to measure the natural frequencies and damping values of all the tested cantilever structures. The GNSS data were processed using relative GNSS positioning and PPP methods. The results obtained using these two methods were also compared with the dynamic characteristics obtained by applying the theoretical and finite element (FE) methods. Furthermore, it is investigated the impact of the stable data length before oscillation events on kinematic PPP. The study showed that the maximum difference among the experimental results in terms of natural frequencies proceeded using PPP is 0.08 Hz when compared with the theoretical and FE results. Furthermore, there is no difference between the PPP and relative GNSS positioning in determining the dynamic behaviour of structures eventhough roving GNSS antenna remains motionless for short-time, such as a few-minutes, before an event occurred. [ABSTRACT FROM AUTHOR]