Abstract
Climate change has increased the number of occurrences of extreme events around the world. Warning and monitoring system is very important for reducing the damage of disasters. The performance of the warning system relies heavily on the quality of data from automated telemetry system (ATS) and the accuracy of the predicting system. Traditional quality management systems cannot discover complicated cases, such as outliers, missing patterns, and inhomogeneity. This paper proposes novel procedures to handle these complex issues in hydrological data focusing on water level. In the proposed system, DBSCAN, which is a clustering algorithm, is applied to discover outliers and missing patterns. The experimental results show that the system outperforms a statistical criterion, mean ± n×SD, where n is a constant. Also, all missing patterns can perfectly be discovered by our approach. For the inhomogeneity problem, several statistical approaches are compared. The comparison results suggest that the best homogenization tool is changepoint, a method based on F-test.
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© 2014 Springer International Publishing Switzerland
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Markpeng, P., Wongnimmarn, P., Champreeda, N., Vateekul, P., Sarinnapakorn, K. (2014). Controlling Quality of Water-Level Data in Thailand. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_51
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DOI: https://doi.org/10.1007/978-3-319-05476-6_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
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