지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
This study was conducted to compare the predictive accuracy of three popular machine learning algorithms, i.e., Bayesian Regularized Neural Networks (BRNN), Neural Network (NN), and Support Vector Machines with Radial Basis Function Kernel (SVM), for modeling river water quality and quantity. Input data with 11 parameters collected from three monitoring sites in the lower part of the Nakdong River between January 2008 to December 2018 were input into the three algorithms. The data were divided into two subsets, which included a training data set and test data set in a ratio of 70:30. The results showed that NN displayed better performance in prediction accuracy than BRNN and SVM when optimizing one or two tuning parameters. The prediction accuracy was, on average, higher for chlorophyll a than for discharge, regardless of which machine learning algorithm was used. Identical results were also observed in the training data set. The two variables biochemical oxygen demand (BOD) and chemical oxygen demand (COD) played an important role in predicting chlorophyll a, whereas suspended solids (SS) was designated as the largest contributor for the prediction of discharge in all three algorithms. In terms of error metrics and performance indices such as root mean squared error (RMSE), mean absolute error (MAE), and R2, the three machine learning algorithms exhibited improved performance in the test data set, as compared to the training data set. We believe that this preliminary study helps identify suitable methods for enhanced prediction of emerging water quality or quantity parameters such as harmful algal blooms.
목차
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참고문헌 (14)
참고문헌 신청
United States Environmental Protection Agency / 2008 / Handbook for developing watershed plans to restore and protect our waters
Ministry of Environment / 2007 / A long-term plan for water quality measurement and water ecosystem survey: 2017-2015
Bae, MS / 2011 / Problems and Improvement Schemes of TMDL Implementation - Water Quality Standards and Development Plans / Journal of Korean Society of Environmental Engineers 33(6):390~395
Gassman, PW / 2007 / The soil and water assessment tool:historical development, applications, and future research directions / Transactions of the ASABE 50(4):1211~1250
Krysanova, V / 2008 / Advances in ecohydrological modelling with SWAT—a review / Hydrological Sciences Journal - Journal des Sciences Hydrologiques 53(5):939~947
Ministry of Environment / 2007 / A long-term plan for water quality measurement and water ecosystem survey: 2017-2015
Bae, MS / 2011 / Problems and Improvement Schemes of TMDL Implementation - Water Quality Standards and Development Plans / Journal of Korean Society of Environmental Engineers 33(6):390~395
Gassman, PW / 2007 / The soil and water assessment tool:historical development, applications, and future research directions / Transactions of the ASABE 50(4):1211~1250
Krysanova, V / 2008 / Advances in ecohydrological modelling with SWAT—a review / Hydrological Sciences Journal - Journal des Sciences Hydrologiques 53(5):939~947
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