Open AccessAplicação de Números Aleatórios Artificiais e Método Monte Carlo na Análise de Confiabilidade de Redes Geodésicas
Author(s) -
Maria Luísa Silva Bonimani,
Vinícius Francisco Rofatto,
Marcelo Tomio Matsuoka,
Ivandro Klein
Publication year - 2019
Publication title -
revista brasileira de computação aplicada
Language(s) - English
Resource type - Journals
ISSN - 2176-6649
DOI - 10.5335/rbca.v11i2.8906
Subject(s) - geodetic datum , reliability (semiconductor) , outlier , monte carlo method , computer science , geolocation , context (archaeology) , geodesy , mathematics , power (physics) , geology , statistics , physics , artificial intelligence , quantum mechanics , world wide web , paleontology
A Geodetic Network is a network of point interconnected by direction and/or distance measurements or by using Global Navigation Satellite System receivers. Such networks are essential for the most geodetic engineering projects, such as monitoring the position and deformation of man-made structures (bridges, dams, power plants, tunnels, ports, etc.), to monitor the crustal deformation of the Earth, to implement an urban and rural cadastre, and others. One of the most important criteria that a geodetic network must meet is reliability. In this context, the reliability concerns the network's ability to detect and identify outliers. Here, we apply the Monte Carlo Method (MMC) to investigate the reliability of a geodetic network. The key of the MMC is the random number generator. Results for simulated closed levelling network reveal that identifying an outlier is more difficult than detecting it. In general, considering the simulated network, the relationship between the outlier detection and identification depends on the level of significance of the outlier statistical test.