Date of Award
2011-01-01
Degree Name
Master of Science
Department
Computer Science
Advisor(s)
Olac Fuentes
Abstract
Assessing the quality of sensor data in environmental monitoring applications is important, as erroneous readings produced by malfunctioning sensors, calibration drift, and problematic climatic conditions, such as icing or dust, are common.Traditional data quality checking and correction is a painstaking manual process, so the development of automatic systems for this task is highly desirable.
This study investigates machine learning methods to identify and clean incorrect data from a real-world environmental sensor network, the Jornada Experimental Range, located in Southern New Mexico. We evaluated several learning algorithms and data replacement schemes, and developed a method to identify the problematic sensor. The evidence found and its analysis allowed us to conclude that learning algorithms are an effective way of cleansing these types of datasets and identifying noisy sensors.
Language
en
Provenance
Received from ProQuest
Copyright Date
2011
File Size
67 pages
File Format
application/pdf
Rights Holder
Gesuri Ramirez
Recommended Citation
Ramirez, Gesuri, "Assessing Data Quality In A Sensor Network For Environmental Monitoring" (2011). Open Access Theses & Dissertations. 2374.
https://scholarworks.utep.edu/open_etd/2374
Included in
Computer Sciences Commons, Ecology and Evolutionary Biology Commons, Environmental Sciences Commons