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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.
Joo Hyun Bae; Jeongho Han; Dongjun Lee; Jae E Yang; Jonggun Kim; Kyoung Jae Lim; Jason C Neff; Won Seok Jang. Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models. Sustainability 2019, 11, 7212 .
AMA StyleJoo Hyun Bae, Jeongho Han, Dongjun Lee, Jae E Yang, Jonggun Kim, Kyoung Jae Lim, Jason C Neff, Won Seok Jang. Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models. Sustainability. 2019; 11 (24):7212.
Chicago/Turabian StyleJoo Hyun Bae; Jeongho Han; Dongjun Lee; Jae E Yang; Jonggun Kim; Kyoung Jae Lim; Jason C Neff; Won Seok Jang. 2019. "Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models." Sustainability 11, no. 24: 7212.
The impact of the channel geometry on water quantity and quality simulation of the Soil and Water Assessment Tool (SWAT) was evaluated for the Andong Dam watershed. The new equations to determine the bankfull width of the channels and the bottom width of the floodplains were developed using aerial photographs, and its performance was compared with the current equations of SWAT. The new equations were more exact than the current equations since the current equations tended to overestimate the widths of the channel and floodplain. When compared with the observed data, the streamflow of the scenario 2 (S2, applying the new equations) showed lower deviation and higher accuracy than scenario 1 (S1, applying the current equations) because the peak flow of S2 captured the observed data better due to the impact of the change geometry. Moreover, the water quality results of S2 outperformed S1 regarding suspended solid, total nitrogen, and dissolved oxygen. This is attributed to the variables, such as flow travel time, which is directly related to the channel geometry. Additionally, SWAT was modified to consider the various channel cross-sectional shapes. The results of this study suggest that the channel geometry information for the water quantity and quality estimation should be carefully applied, which could improve the model performance regarding streamflow and water quality simulations.
Jeongho Han; Dongjun Lee; Seoro Lee; Se-Woong Chung; Seong Joon Kim; Minji Park; Kyoung Jae Lim; Jonggun Kim. Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed. Water 2019, 11, 718 .
AMA StyleJeongho Han, Dongjun Lee, Seoro Lee, Se-Woong Chung, Seong Joon Kim, Minji Park, Kyoung Jae Lim, Jonggun Kim. Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed. Water. 2019; 11 (4):718.
Chicago/Turabian StyleJeongho Han; Dongjun Lee; Seoro Lee; Se-Woong Chung; Seong Joon Kim; Minji Park; Kyoung Jae Lim; Jonggun Kim. 2019. "Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed." Water 11, no. 4: 718.