Real-time precise point positioning (PPP) has become a prevalent technique in global navigation satellite systems (GNSS). However, GNSS real-time users must receive space state representation (SSR) products to correct for satellite clock, orbit, and phase biases. The International GNSS Service (IGS) provides GNSS users with real-time services (RTSs) through different real-time correction SSR products. These products arrive at the GNSS users with some latency, which affects the quality of real-time PPP positioning. The autoregressive integrated moving average (ARIMA) and support vector regression (SVR) models are used in this research to predict those corrections to eliminate the latency effect. ARIMA model reduces the standard deviation by 28% and 13% for GPS and GLONASS constellations, respectively, compared to the real-time solution, which includes the latency effect, the research simulated the latency effect and named it a forced-latency solution, and the SVR model reduces the standard deviation by 28% and 23% for GPS and GLONASS constellations, respectively. The results for the permanent GNSS stations used in this study across different years 2013, 2014, 2015, 2019, and 2021 show a mean reduction in the 3D positioning standard deviation by 13% compared with the forced-latency solution for the ARIMA solution and 9% for the SVR solution. The potential of both models to overcome the latency effect is apparent based on the findings.

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Early Warning System (EWS) for monitoring megastructures deformation, natural hazards, earthquakes, and landslidescan prevent economic and life losses. Nowadays, Real-Time Precise Point Positioning (RT-PPP) plays a vital role in thisdomain since it relies on precise real-time measurements derived from a single receiver, provides real-time monitoring andglobal coverage. Nevertheless, RT-PPP measurements and methodology is very sensitive to outliers in products, latenciesand changes in the constellation geometry. Consequently, there are long initialization periods, losses of convergence anddifferent noise sources, with a high impact on the warning system's availability or even led out to initiate false warnings.This study presents the first experiment to propose a methodology that can help the decision-makers confirm the warningbased on the probability of the detected movement by using machine learning classification models. For this, in the firstexperiment, a laser engraving machine device was modified to simulate deformations. A control unit will be designed basedon open-source software, Python libraries are implemented, and the G programming language used to control the devicemotions. All this research will be the background on which the early warning service will be developed.