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Article

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

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Korea Water Environment Research Institute, Chuncheon-si, Gangwon-do 24408, Korea
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Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea
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Department of Biological Environment, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea
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Sustainability Innovation Lab at Colorado (SILC), University of Colorado Boulder, Boulder, CO 80303, USA
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(24), 7212; https://doi.org/10.3390/su11247212
Received: 19 November 2019 / Revised: 4 December 2019 / Accepted: 8 December 2019 / Published: 16 December 2019
The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process. View Full-Text
Keywords: machine learning; nonlinear regression algorithms; vegetation filter strips; VFSMOD-W machine learning; nonlinear regression algorithms; vegetation filter strips; VFSMOD-W
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MDPI and ACS Style

Bae, J.H.; Han, J.; Lee, D.; Yang, J.E.; Kim, J.; Lim, K.J.; Neff, J.C.; Jang, W.S. Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models. Sustainability 2019, 11, 7212. https://doi.org/10.3390/su11247212

AMA Style

Bae JH, Han J, Lee D, Yang JE, Kim J, Lim KJ, Neff JC, Jang WS. Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models. Sustainability. 2019; 11(24):7212. https://doi.org/10.3390/su11247212

Chicago/Turabian Style

Bae, Joo H., Jeongho Han, Dongjun Lee, Jae E. Yang, Jonggun Kim, Kyoung J. Lim, Jason C. Neff, and Won S. Jang. 2019. "Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models" Sustainability 11, no. 24: 7212. https://doi.org/10.3390/su11247212

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