Tesis
Criação de um ambiente computacional para detecção de outliers e preenchimento de falhas em dados meteorológicos
Fecha
2015-02-27Registro en:
VENTURA, Thiago Meirelles. Criação de um ambiente computacional para detecção de outliers e preenchimento de falhas em dados meteorológicos. 2015. 96 f. Tese (Doutorado em Física Ambiental) - Universidade Federal de Mato Grosso, Instituto de Física, Cuiabá, 2015.
Autor
Figueiredo, Josiel Maimone de
Nogueira, Marta Cristina de Jesus Albuquerque
http://lattes.cnpq.br/8280601583280522
http://lattes.cnpq.br/1242386923227672
Figueiredo, Josiel Maimone de
568.019.391-49
http://lattes.cnpq.br/1242386923227672
Nogueira, Marta Cristina de Jesus Albuquerque
531.387.751-87
http://lattes.cnpq.br/8280601583280522
568.019.391-49
531.387.751-87
Martins, Claudia Aparecida
696.194.416-72
http://lattes.cnpq.br/0252766947347684
Novais, Jonathan Willian Zangeski
016.698.881-26
http://lattes.cnpq.br/5665663207008673
Barioni, Maria Camila Nardini
271.753.308-71
http://lattes.cnpq.br/3785426518998830
Institución
Resumen
In order to study the environment meteorological data series must be
analyzed. However, these data series may contain errors, because of electronic
failures, animal action or weather phenomena, among other factors. These failures can result in missing data or outliers, causing difficulties in the data analysis.
Therefore, it is important to detect the outliers in the data series and fill in the
missing data. This work presents a computational environment that will enable
the correction of environmental data. In order to achieve this, three new methods
were created in this work: one for gap filling and two for outlier detection. In
addition, three other methods were obtained from other studies and were implemented together with the new methods in a single framework. These methods
use techniques from the area of artificial intelligence and statistics, which often
requires a deep study in order to apply them. However, the developed framework
enables the application of these methods, only demanding the configuration of
some parameters. Thus, the framework allows the development of applications
with functionalities of gap filling and outlier detection. To demonstrate the applicability of these methods a web-based application was developed integrated
with the framework. Besides, tests were carried out to verify the performance
of each method created compared with those obtained from other studies. It is
expected that this structure will increase the quality of data series, assisting in
several scientific researches.