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http://dx.doi.org/10.5389/KSAE.2017.59.4.097

Study of Selection of Regression Equation for Flow-conditions using Machine-learning Method: Focusing on Nakdonggang Waterbody  

Kim, Jonggun (Department of Regional Infrastructures Engineering, Kangwon National University)
Park, Youn Shik (Department of Rural Construction Engineering, Kongju National University)
Lee, Seoro (Department of Regional Infrastructures Engineering, Kangwon National University)
Shin, Yongchul (School of Agricultural Civil and Bio-Industrial Engineering, Kyungpook National University)
Lim, Kyoung Jae (Department of Regional Infrastructures Engineering, Kangwon National University)
Kim, Ki-sung (Department of Regional Infrastructures Engineering, Kangwon National University)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.59, no.4, 2017 , pp. 97-107 More about this Journal
Abstract
This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.
Keywords
LOADEST model; Machine-learning; Pollutant load; Flow conditions; Regression equation;
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Times Cited By KSCI : 3  (Citation Analysis)
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