Análise da precisão dos modelos do método dos mínimos quadrados no ajustamento de dados altimétrico

Abstract

This study addresses the analysis of the accuracy of Least Squares Method (LSM) models applied to the adjustment of altimetric data in a closed traverse. The main objective is to compare the accuracy of three LSM models (Parametric, Correlates, and Combined) and identify which of them provides the best results in altimetric data adjustment. Computational tools such as GNU Octave and Microsoft Excel were used to perform the adjustments and evaluate the precision of the models. The methodology involved applying the models to a set of altimetric data, followed by assessing the accuracy of each one, focusing on the measurement of standard deviations and the consistency of the results. The analysis was complemented by the development of Variance-Covariance Matrix (VCM) graphs, enabling the visualization of the differences among the models. The results indicated that, for the data used in this study, the Correlates model was the most accurate, presenting the lowest standard deviations compared to the Parametric and Combined models. On the other hand, the Combined model showed lower accuracy, demonstrating that the combination of models does not necessarily lead to better performance. Although the Correlates model stood out in this study, it is important to note that, in other contexts and with different datasets, the Parametric or Combined models may perform better depending on the specific conditions of each situation.