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Water
Volume 13
Issue 13
10.3390/w13131862
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Article

Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series

by
Lattawit Kulanuwat
1,
Chantana Chantrapornchai
1,
Montri Maleewong
2,3,
Papis Wongchaisuwat
3,4,*,
Supaluk Wimala
5,
Kanoksri Sarinnapakorn
5 and
Surajate Boonya-aroonnet
5
1
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
2
Department of Mathematics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
3
Excellent Center for Big Data Analytics on Food and Agriculture, Kasetsart University, Bangkok 10900, Thailand
4
Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
5
Hydro-Informatics Institute, Bangkok 10900, Thailand
*
Author to whom correspondence should be addressed.
Academic Editor: Anis Younes
Water 2021, 13(13), 1862; https://doi.org/10.3390/w13131862
Received: 3 June 2021 / Revised: 25 June 2021 / Accepted: 30 June 2021 / Published: 3 July 2021
(This article belongs to the Section Hydrology)
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Abstract

Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal pattern distribution. We developed a median-based statistical outlier detection approach using a sliding window technique. In order to fill anomalies, various interpolation techniques were considered. Our proposed framework exhibited promising results after evaluating with F1-score and root mean square error (RMSE) based on our artificially induced data points. The present system can also be easily applied to various patterns of hydrological time series with diverse choices of internal methods and fine-tuned parameters. Specifically, the Spline interpolation method yielded a superior performance on non-cyclical data while the long short-term memory (LSTM) outperformed other interpolation methods on a distinct tidal data pattern. View Full-Text
Keywords: water management; anomaly detection; data imputation; time series; sliding window; median absolute deviation; LSTM water management; anomaly detection; data imputation; time series; sliding window; median absolute deviation; LSTM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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MDPI and ACS Style

Kulanuwat, L.; Chantrapornchai, C.; Maleewong, M.; Wongchaisuwat, P.; Wimala, S.; Sarinnapakorn, K.; Boonya-aroonnet, S. Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series. Water 2021, 13, 1862. https://doi.org/10.3390/w13131862

AMA Style

Kulanuwat L, Chantrapornchai C, Maleewong M, Wongchaisuwat P, Wimala S, Sarinnapakorn K, Boonya-aroonnet S. Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series. Water. 2021; 13(13):1862. https://doi.org/10.3390/w13131862

Chicago/Turabian Style

Kulanuwat, Lattawit, Chantana Chantrapornchai, Montri Maleewong, Papis Wongchaisuwat, Supaluk Wimala, Kanoksri Sarinnapakorn, and Surajate Boonya-aroonnet. 2021. "Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series" Water 13, no. 13: 1862. https://doi.org/10.3390/w13131862

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