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Water 2019, 11(4), 764; https://doi.org/10.3390/w11040764

Filtering Continuous River Surface Velocity Radar Data

1
National Center for High-Performance Computing, National Applied Research Laboratories, No. 22 Keyuan Road, Situn District, Taichung City 40763, Taiwan
2
Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, No. 22 Keyuan Road, Situn District, Taichung City 40763, Taiwan
3
Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan
4
Department of Geography, University of Eswatini, P/Bag 4, Kwaluseni M201, Eswatini
5
Sustainability Center, Nanhua University, Chiayi County 62249, Taiwan
*
Author to whom correspondence should be addressed.
Received: 15 January 2019 / Revised: 19 March 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
(This article belongs to the Section Hydraulics)
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Abstract

In this study, the prediction interval method was used in simple regression models to filter continuous river surface velocity microwave radar data. To evaluate the model performance, two data sets from monitoring stations with mild and steep channel slopes were used. A human–machine interface software program developed in LabVIEW was used to sample data from big continuous data for establishing the relationships between surface velocity and water level, two surface velocities, and their prediction intervals. Filtering by coupled relationships detected the most noise in the surface velocity and the original data, and the results for different cases were compared. The results were also compared with widely used modern smoothing methods. It was found that raw data cannot always be post–processed using these smoothing methods. Moreover, peaks become distorted. This study provides a method for filtering noise signals in continuous river surface velocity data without data contamination, which makes the surface velocity data more reliable and applicable for advanced studies, such as machine learning applications, and can be applied for the quality control of surface velocity data in the future. View Full-Text
Keywords: prediction interval; continuous river surface velocity; smoothing methods; despiking prediction interval; continuous river surface velocity; smoothing methods; despiking
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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 (CC BY 4.0).
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Wang, H.-W.; Lin, G.-F.; Tfwala, S.S.; Hong, J.-H. Filtering Continuous River Surface Velocity Radar Data. Water 2019, 11, 764.

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