PROPOSED METHODOLOGY FOR ESTABLISHING AN EARLY GNSS WARNING SYSTEM FOR REAL-TIME DEFORMATION MONITORING

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.