JKSWE

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Alizamir M., Heddam S., Kim S., Mehr A. D., 2021, On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: Case studies of river and lake in USA, Journal of Cleaner Production, Vol. 285, pp. 124868

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An Y. J., Kampbell D. H., 2003, Monitoring chlorophyll a as a measure of algae in lake Texoma marinas, Bulletin of Environmental Contamination and Toxicology, Vol. 70, No. 3, pp. 606-611

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Bae S. W., Yu J. S., 2018, Predicting the real estate price index using machine learning methods and time series analysis model, [Korean Literature], Housing Studies Review, Vol. 26, No. 1, pp. 107-133

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Breiman L., 2001, Random forests, Machine Learning, Vol. 45, No. 1, pp. 5-32

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Cha Y., Shin J., Kim Y., 2020, Data-driven modeling of freshwater aquatic systems: Status and prospects, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 36, No. 6, pp. 611-620

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Chen T., Guestrin C., 2016, Xgboost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794

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Choi M. S., Kim C. H., Park H. M., Cheon M. A., Yoon H., Namgoong Y., Kim J. H., 2020, Detecting errors in POS-Tagged corpus on XGBoost and cross validation, [Korean Literature], KIPS Transactions on Software and Data Engineering, Vol. 9, No. 7, pp. 221-228

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Chun B., Lee T., Kim S., Kim J., Jang K., Chun J., Shin Y., 2020, Estimation of DNN-based Soil moisture at mountainous regions, [Korean Literature], Journal of The Korean Society of Agricultural Engineers, Vol. 62, No. 5, pp. 93-103

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Chung D. H., Yun J. S., Yang S. M., 2021, Machine learning for predicting entrepreneurial innovativeness, [Korean Literature], Asia-Pacific Journal of Business Venturing and Entrepreneurship, Vol. 16, No. 3, pp. 73-86

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Cui Y., Meng F., Fu P., Yang X., Zhang Y., Liu P., 2021, Application of hyperspectral analysis of chlorophyll a concentration inversion in Nansi lake, Ecological Informatics, Vol. 64, pp. 101360

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Dittman D. J., Khoshgoftaar T. M., Napolitano A., 2015, 2015 IEEE International Conference on Information Reuse and Integration, IEEE, pp. 457-463

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Friedman J. H., Popescu B. E., 2003, Importance sampled learning ensembles, Journal of Machine Learning Research, Vol. 94305, pp. 1-32

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Gnauck A., 2004, Interpolation and approximation of water quality time series and process identification, Analytical and Bioanalytical Chemistry, Vol. 380, No. 3, pp. 484-492

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Ha J. E., Shin H. C., Lee Z. K., 2017, Korean text classification using randomforest and XGBoost focusing on Seoul metropolitan civil complaint data, [Korean Literature], The Journal of Bigdata, Vol. 2, No. 2, pp. 95-104

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Han J. H., Ko D. K., Choe H., 2019, Predicting and analyzing factors affecting financial stress of household using machine learning: Application of Xgboost, [Korean Literature], Journal of Consumer Studies, Vol. 30, No. 2, pp. 21-43

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Han S. H., Kim Y. Y., Sung Y. G., Park I. B., Cho D. H., Nam W. K., Oh J. K., 2015, Characteristics of organics and ammonia nitrogen discharged by pollution source from human living, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 31, No. 4, pp. 377-386

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He Y., Wang X., Xu F., 2022, How reliable is chlorophyll-a as algae proxy in lake environments? New insights from the perspective of n-alkanes, Science of The Total Environment, Vol. 836, pp. 155700

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Jeong J. H., Jeong Y. C., Chae T. Y., 2021, Feature importance of electricity consumption for highly energy demand commercial buildings in cooling season, [Korean Literature], Journal of The Korean Society of Living Environmental System, Vol. 28, No. 1, pp. 29-38

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Jung S. Y., Kim I. K., 2017a, Analysis of water quality factor and correlation between water quality and Chl-a in middle and downstream weir section of Nakdong river, [Korean Literature], Journal of Korean Society of Environmental Engineers, Vol. 39, No. 2, pp. 89-96

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Jung S. Y., Kim I. K., 2017b, Analysis of the water quality and correlation of impact factors during summer season in changnyeong-haman weir section, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 31, No. 1, pp. 83-91

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Junninen H., Niska H., Tuppurainen K., Ruuskanen J., Kolehmainen M., 2004, Methods for imputation of missing values in air quality data sets, Atmospheric Environment, Vol. 38, No. 18, pp. 2895-2907

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Kang B. K., Park J., 2021, Effect of input variable characteristics on the performance of an ensemble machine learning model for algal bloom prediction, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 35, No. 6, pp. 417-424

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Kang K. H., Park H. J., 2019, Study on the effect of training data sampling strategy on the accuracy of the landslide susceptibility analysis using random forest method, [Korean Literature], Economic and Environmental Geology, Vol. 52, No. 2, pp. 199-212

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Karaca Y., Baleanu D., 2020, A novel R/S fractal analysis and wavelet entropy characterization approach for robust forecasting based on self-similar time series modeling, Fractals, Vol. 28, No. 08, pp. 2040032

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Kim C. W., Seo Y. G., 2020, Design and performance prediction of ultra-low flow hydrocyclone using the random forest method, [Korean Literature], Journal of the Korean Society of Manufacturing Technology Engineers, Vol. 29, No. 2, pp. 83-88

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Kim G. H., Jung K. Y., Yoon J. S., Cheon S. U., 2013, Temporal and spatial analysis of water quality data observed in lower watershed of Nam river dam, [Korean Literature], Journal of the Korean Society of Hazard Mitigation, Vol. 13, No. 6, pp. 429-438

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Kim H. I., Lee Y. S., Kim B., 2021, Real-time flood prediction applying random forest regression model in urban areas, [Korean Literature], Journal of Korea Water Resources Association, Vol. 54, No. spc1, pp. 1119-1130

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Kim J., Kim J., Seo D., 2020, Effect of major pollution sources on algal blooms in the Seungchon weir and Juksan weir in the Yeongsan river using EFDC, [Korean Literature], Journal of Korea Water Resources Association, Vol. 53, No. 5, pp. 369-381

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Kim K. M., Ahn J. H., 2022, Machine learning predictions of chlorophyll-a in the Han river basin, Korea, Journal of Environmental Management, Vol. 318, pp. 115636

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Kim S. H., Park J. H., Kim B., 2021, Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning, [Korean Literature], Journal of Korea Water Resources Association, Vol. 54, No. spc1, pp. 1167-1181

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Kim S. W., Jun S. H., 2019, AI technology analysis using variable importance of deep learning, [Korean Literature], Journal of the Korean Institute of Intelligent Systems, Vol. 29, pp. 70-75

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Kim Y., Kwak G. H., Lee K. D., Na S. I., Park C. W., Park N. W., 2018, Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size, [Korean Literature], Korean Journal of Remote Sensing, Vol. 34, No. 5, pp. 811-827

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Korea Environment Institute (KEI), 2020, Development and application of algal bloom using artificial intelligence deep learning, https://www.kei.re.kr/elibList.es?mid=a10101000000&elibName=researchreport&act=view&c_id=732914 (accessed Dec. 2020)

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Korea Meteorological Administration (KMA), 2022, Open MET Data Portal (OMDP), https://data.kma.go.kr/ (accessed Jun. 2022)

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Kriegeskorte N., Golan T., 2019, Neural network models and deep learning, Current Biology, Vol. 29, No. 7, pp. R231-R236

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Kwak J., 2021, A study on the 3-month prior prediction of Chl-a concentraion in the Daechong lake using hydrometeorological forecasting data, [Korean Literature], Journal of Wetlands Research, Vol. 23, No. 2, pp. 144-153

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K-water, 2022, My Water, https:/www.water.or.kr/ (accessed Jun. 2022)

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Lee K. T., Kim M. S., Kim H. J., Kim J. H., 2021, A model to predict occupational safety and health management expenses in construction applying multi-variate regression analysis and deep neural network, [Korean Literature], Journal of the Architectural Institute of Korea, Vol. 37, No. 9, pp. 217-226

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Lee S. M., Kim I. K., 2021, A study on applying random forest and gradient boosting algorithm for Chl-a prediction of Daecheong lake, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 35, No. 6, pp. 507-516

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Lee S. M., Park K. D., Kim I. K., 2020, Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong river (focusing on water quality and quantity factors), [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 34, No. 4, pp. 277-288

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Lee Y., Sun J., 2020, Predicting highway concrete pavement damage using XGBoost, [Korean Literature], Korean Journal of Construction Engineering and Management, Vol. 21, No. 6, pp. 46-55

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Lee Y. G., Oh J. Y., Kim G., 2020, Interpretation of load forecasting using explainable artificial intelligence techniques, [Korean Literature], The Transactions of the Korean Institute of Electrical Engineers, Vol. 69, No. 3, pp. 480-485

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Lee Y. J., Jeong B. K., Shin Y. S., Kim S. H., Shin K. H., 2013, Determination of the origin of particulate organic matter at the estuary of Youngsan river using stable isotope ratios (δ13C, δ15N), [Korean Literature], Korean Journal of Ecology and Environment, Vol. 46, No. 2, pp. 175-184

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Lepot M., Aubin J. B., Clemens F. H., 2017, Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment, Water, Vol. 9, No. 10, pp. 796

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Lim J. S., Kim Y. W., Lee J. H., Park T. J., Byun I. G., 2015, Evaluation of correlation between chlorophyll-a and multiple parameters by multiple linear regression analysis, [Korean Literature], Journal of Korean Society of Environmental Engineers, Vol. 37, No. 5, pp. 253-261

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Liu X., Feng J., Wang Y., 2019, Chlorophyll a predictability and relative importance of factors governing lake phytoplankton at different timescales, Science of the Total Environment, Vol. 648, pp. 472-480

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Ma J., Qin B., Paerl H. W., Brookes J. D., Hall N. S., Shi K., Long S., 2016, The persistence of cyanobacterial (M icrocystis spp.) blooms throughout winter in lake Taihu, China, Limnology and Oceanography, Vol. 61, No. 2, pp. 711-722

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Ministry of Environment (ME), 2022, Water Environment Information System (WEIS), https://water.nier.go.kr/ (accessed Jun. 2022)

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Müller A. C., Guido S., 2016, Introduction to machine learning with Python: A guide for data scientists, O’Reilly Media, Inc, pp. 386

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Noh S., Park H., Choi H., Lee J., 2014, Effect of climate change for cyanobacteria growth pattern in Chudong station of Lake Daechung, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 30, No. 4, pp. 377-385

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Oh J. Y., Ham D. H., Lee Y. G., Kim G., 2019, Short-term load forecasting using XGBoost and the analysis of hyperparameters, [Korean Literature], The Transactions of the Korean Institute of Electrical Engineers, Vol. 68, pp. 1073-1078

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Park H. K., Byeon M. S., Choi M. J., Kim Y. J., 2008, The effect factors on the growth of phytoplankton and the sources of organic matters in downstream of South-Han river, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 24, No. 5, pp. 556-562

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Park J., 2022, Development of ensemble machine learning model considering the characteristics of input variables and the interpretation of model performance using explainable artificial intelligence, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 36, No. 4, pp. 239-248

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Park Y., Cho K. H., Park J., Cha S. M., Kim J. H., 2015, Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea, [Korean Literature], Science of the Total Environment, Vol. 502, pp. 31-41

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Savitzky A., Golay M. J., 1964, Smoothing and differentiation of data by simplified least squares procedures, Analytical chemistry, Vol. 36, No. 8, pp. 1627-1639

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Schuwirth N., Borgwardt F., Domisch S., Friedrichs M., Kattwinkel M., Kneis D., Vermeiren P., 2019, How to make ecological models useful for environmental management, Ecological Modelling, Vol. 411, pp. 108784

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Seo K., Na J. E., Ryu H. S., Kim K., 2018, Characteristics of nitro-nutrients and phytoplankton dynamics in the Yeongsan river after weir construction, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 34, No. 4, pp. 423-430

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Shin J. K., Kang B. G., Hwang S. J., 2016, Limnological study on spring-bloom of a green algae, eudorina elegans and weirwater pulsed-flows in the midstream (Seungchon weir pool) of the Yeongsan river, Korea, [Korean Literature], Korean Journal of Ecology and Environment, Vol. 49, No. 4, pp. 320-333

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Shin Y., Lee H., Lee Y. J., Seo D. K., Jeong B., Hong S., Heo T. Y., 2019, The prediction of diatom abundance by comparison of various machine learning methods, Mathematical Problems in Engineering, Vol. 2019, pp. 1-13

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Shin Y., Yu H., Lee H., Lee D., Park G., 2015, The change in patterns and conditions of algal blooms resulting from construction of weirs in the Youngsan river: Long-term data analysis, [Korean Literature], Korean Journal of Ecology and Environment, Vol. 48, No. 4, pp. 238-252

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Sim D., Lee J. Y., Jang J., Lee M., 2022, Prediction of chloride concentration in groundwater on Jeju Island using XGBoost regression machine learning, [Korean Literature], Journal of the Geological Society of Korea, Vol. 55, No. 2, pp. 243-256

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Singha S., Pasupuleti S., Singha S. S., Singh R., Kumar S., 2021, Prediction of groundwater quality using efficient machine learning technique, Chemosphere, Vol. 276, pp. 130265

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Song J. J., Kim B. B., Hong S. G., 2015, Study on water quality change of Yeongsan river's upstream, [Korean Literature], Journal of Korean Society of Environmental Technology, Vol. 16, No. 2, pp. 154-159

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Tekile A., Kim I., Kim J., 2015, Mini-review on river eutrophication and bottom improvement techniques, with special emphasis on the Nakdong river, Journal of Environmental Sciences, Vol. 30, pp. 113-121

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Tyralis H., Papacharalampous G., Langousis A., 2019, A brief review of random forests for water scientists and practitioners and their recent history in water resources, Water, Vol. 11, No. 5, pp. 910

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Wetzel R. G., Likens G. E., 2013, Limnological Analyses, third ed, Springer Science & Business Media

JKSWE The Journal of
the Korean Society on Water Environment

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JKSWE The Journal ofthe Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal of
the Korean Society on Water Environment