Correcting Systematic Underprediction of Biochemical Oxygen Demand in Support Vector Regression
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Environmental Engineering
Volume 143, Issue 9
Abstract
Biochemical oxygen demand (BOD) is a variable that is missing or inaccurate in many water quality data sets because of difficulties in diluting highly polluted water samples. Machine learning algorithms, particularly support vector regression (SVR), are useful to build regression models to fill gaps in these data sets. The SVR can underpredict extreme-high values when they are few in number and underrepresented. This paper evaluates two methods, bootstrapping and data expansion, to mitigate the problem by increasing the proportion of extreme-high BOD in the data set before training the gap-filling model. Both methods were tested on the water quality data of Yuen Long Creek, Hong Kong, for the years 2000–2014. Both methods were effective in mitigating systematic underprediction and reducing their residual errors when the proportion of extreme-high values in the data set were increased from 3 to 30–40%. Both methods were useful for gap filling on BOD time series because extreme-high values are often the ones missing or inaccurate when highly polluted samples are diluted.
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Published In
Journal of Environmental Engineering
Volume 143 • Issue 9 • September 2017
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©2017 American Society of Civil Engineers.
History
Received: Dec 7, 2016
Accepted: Feb 7, 2017
Published online: May 8, 2017
Published in print: Sep 1, 2017
Discussion open until: Oct 8, 2017
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