Machine Learning Models for Identifying Patterns in GNSS Meteorological Data

This research is centered on the comprehensive analysis of meteorological data sourced from strategically positioned Global Navigation Satellite System (GNSS) stations located in Ecuador. Meteorological data of LJEC, PLEC, CUEC, and GZEC was collected and analyzed. For each station, three years (2017–2019) meteorological data recorded throughout each year at one-second intervals were analyzed. Data mining techniques are employed for in-depth analysis, utilizing machine learning algorithms to discern these stations behavior patterns. A machine learning model has been meticulously developed and fine-tuned to harmonize with the data’s underlying structure, thereby yielding precise results with a minimal margin of error. After model development and requisite testing, a satisfaction rate of 90% has been achieved, affirming formulated hypotheses validation.