The Journal of
the Korean Society on Water Environment

The Journal of
the Korean Society on Water Environment

Bimonthly
  • ISSN : 2289-0971 (Print)
  • ISSN : 2289-098X (Online)
  • KCI Accredited Journal

Editorial Office

References

1 
Ahn C. Y., Oh H. M., Park Y. S., 2011, Evaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks, Journal of Phycology, Vol. 47, No. 3, pp. 495-504DOI
2 
Ahu R., Yang L., Liu T., Wen X., Zhang L., Chang Y., 2019, Hydrological responses to the future climate change in a data scarce region, Northwest China: Application of machine learning models, Water, Vol. 11, No. 8, pp. 1588DOI
3 
Bhandari N. S., Nayal K., 2008, Correlation study on physico-chemical parameters and quality assessment of Kosi river water, Uttarakhand, E-Journal of Chemistry, Vol. 5, No. 2, pp. 342-346DOI
4 
Brieman L., 2001, Random forests, Machine Learning, Vol. 45, pp. 5-32Google Search
5 
Cho K., Van Merrienboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation, 1406.1078, arXiv preprint arXivGoogle Search
6 
Choi H., Suh S. I., Kim S. H., Han E. J., Ki S. J., 2021, Assessing the performance of deep learning algorithms for short-term surface water quality prediction, Sustainability, Vol. 13, No. 19, pp. 10690DOI
7 
Garcia-Nieto P. J., Garcia-Gonzalo E., Alonso Fernandez J. R., Diaz Muniz C., 2020, A new predictive model for evaluating Chlorophyll-a concentration in Tanes reservoir by using a gaussian process regression, Water Resources Management, Vol. 34, pp. 4921-4941DOI
8 
Herzsprung P., Wentzky V., Kamjunke N., Tumpling W., Wilske C., Friese K., Boehrer B., Reemtsma T., Rinke K. J., Lechtenfeld O., 2020, Improved understanding of dissolved organic matter processing in freshwater using complementary experimental and machine learning approaches, Environment Science & Technology, Vol. 54, No. 21, pp. 13556-13565DOI
9 
Hochreiter S., Schmidhuber J., 1997, Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780DOI
10 
Hu Z., Zhang Y., Zhao Y., Xie M., Zhong J., Tu Z., Liu J., 2019, A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture, Sensors, Vol. 19, No. 6, pp. 1420DOI
11 
Jo B. G., Jung W. S., Lee J. M., Kim Y. D., 2022, Analysis of water quality impact of Hapcheon dam reservoir according to changes in watershed runoff using ANN, [Korea Literature], Journal of Wetlands Research, Vol. 24, No. 1, pp. 25-37Google Search
12 
Kim M., Lee J., Sung K., Lim C., Hwang W., Hyun S., 2022, Potential impacts of climate change on water temperature of the streams in Han-river basin, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 38, No. 1, pp. 19-30Google Search
13 
Kong D., 2019, Evaluating effect of density flow from upstream on vertical distribution of water quality at the Paldang reservoir, [Korean Literature], Journal of Korea Society on Water Environment, Vol. 35, No. 6, pp. 557-566Google Search
14 
Kuo J. T., Hsieh M. H., Lung W. S., She N., 2007, Using artificial neural network for reservoir eutrophication prediction, Ecological Modelling, Vol. 200, No. 1-2, pp. 171-177DOI
15 
Kwak J., 2021, A study on the 3-month prior prediction of Chl-a concentration in the Daechong lake using hydrometeorological forecasting data, [Korean Literature], Journal of Wetlands Research, Vol. 23, No. 2, pp. 144-153Google Search
16 
Lee J. H., Lee J. Y., Lee M. H., Lee M. Y., Kim Y. W., Hyung J. S., Kim K. B., Cha Y. K., Koo J. Y., 2022, Development of a short-term water quality prediction model for urban rivers using real-time water quality data, Water Supply, Vol. 22, No. 4, pp. 4082-4097DOI
17 
Li L., Jamieson K., DeSalvo G., Rostamizadeh A., Talwalkar A., 2018, Hyperband: A novel bandit-based approach to hyperparameter optimization, Journal of Machine Learning Research, Vol. 18, No. 1, pp. 6765-6816Google Search
18 
Li M., Xie G. Q., Dai C. R., Yu L. X., Li F. R., Yang S. P., 2009, A study of the relationship between the water body chlorophyll-a and water quality factors of the offcoast of Dianchi lake, Yunnan Geographic Environment Research, Vol. 21, No. 2, pp. 102-106Google Search
19 
Liu P., Wang J., Sangaiah A., Xie Y., Yin X., 2019, Analysis and prediction of water quality using LSTM deep neural networks in IoT environment, Sustainability, Vol. 11, No. 7, pp. 2058DOI
20 
Lopez-Archilla A., Moreira D., Lopez-Garcia P., Guerrero C., 2004, Phytoplankton diversity and cyanobacterial dominance in a hypereutrophic shallow lake with biologically produced alkaline pH, Extremophiles, Vol. 8, No. 2, pp. 109-115DOI
21 
Lu H., Ma X., 2020, Hybrid decision tree-based machine learning models for short-term water quality prediction, Chemosphere, Vol. 249, pp. 126169DOI
22 
Luo W., Zhu S., Wu S., Dai J., 2019, Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes, Environmental Science and Pollution Research, Vol. 26, No. 29, pp. 30524-30532Google Search
23 
Min Y. H., Hyun D. Y., Eum C. H., Chung N., Kang S. W., Lee S., 2011, A study on relationship of concentration of phosphorus, turbidity and pH with temperature in water and soil, Analytical Science and Technology, Vol. 24, No. 5, pp. 378-386DOI
24 
Moheimani N. R., Borowitzka M. A., 2006, The long-term culture of the coccolithophore Pleurochrysis carterae (Haptophyta) in outdoor raceway ponds, Journal of Applied Phycology, Vol. 18, No. 6, pp. 703-712DOI
25 
National Institute of Environmental Research (NIER), 2017, A comprehensive study on water quality control of Paldang watershed(Ⅲ): a assessment system to diagnose and evaluate water pollutant, [Korea Literature], National Institute of Environmental Research, NIER-RP2017-223Google Search
26 
National Institute of Environmental Research (NIER), 2021, Prediction on water quality variations in Paldang Reservoir by climate change(Ι): Application of Data-based model through high frequency monitoring, [Korea Literature], National Institute of Environmental Research, NIER-RP2021-138, pp. 1-50Google Search
27 
Park J., 2021, The effect of input variables clustering on the characteristics of ensemble machine learning model for water quality prediction, [Korea Literature], Journal of Korean Society on Water Environment, Vol. 37, No. 5, pp. 335-343Google Search
28 
Park J., Lee H., 2020, Prediction of high turbidity in rivers using LSTM algorithm, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 34, No. 1, pp. 35-43DOI
29 
Park S. J., Lee D. K., 2020, Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms, Environmental Research Letters, Vol. 15, No. 9, pp. 094052DOI
30 
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, Science of the Total Environment, Vol. 502, pp. 31-41DOI
31 
Rixen T., Baum A., Sepryani H., Pohlmann T., Jose C., Samiaji J., 2010, Dissolved oxygen and its response to eutrophication in a tropical black water river, Journal of Environmental Management, Vol. 91, No. 8, pp. 1730-1737DOI
32 
Ruan X. H., Shi X. D., Zhao Z. H., Ni L. X., Wu Y., Jiao T., 2008, Correlation between chlorophyll-a concentration and environmental factors in shallow lakes in plain river network areas of Suzhou, Journal of Lake Sciences, Vol. 20, No. 5, pp. 556-562DOI
33 
Saraceno J. F., Pellerin B. A., Downing B. D., Boss E., Bachand P. A., Bergamaschi B. A., 2009, High-frequency in situ optical measurements during a storm event: assessing relationships between dissolved organic matter, sediment concentrations, and hydrologic processes, Journal of Geophysical research, Vol. 114, No. G4DOI
34 
Seo H. J., Kang I. S., Son K. R., Eun Y., Jeong W. S., Kim S. J., 2019, Evaluation of water quality characteristics using multivariate statistical analysis in the fourth reservoir, Journal of Environmental Analysis, Health and Toxicology, Vol. 22, No. 3, pp. 117-125DOI
35 
Shim S. H., Kim Y. H., Lee H. W., Kim M., Choi J. H., 2022, Comparison of chlorophyll-a prediction and analysis of influential factors in Yeongsan river using machine learning and deep learning, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 38, No. 6, pp. 292-305Google Search
36 
Singh K. P., Basant N., Gupta S., 2011, Support vector machines in water quality management, Analytica Chimica Acta, Vol. 703, No. 2, pp. 152-162DOI
37 
Wang X., Zhang H., Bertone E., Stewart R., Hughes S., 2021, Coupled data-driven and process-based model for fluorescent dissolved organic matter prediction in a shallow subtropical reservoir, Environmental Modelling & Software, Vol. 141, pp. 105053DOI
38 
Woo J. W., Kim Y. J., Yoon J. S., 2022, Prediction of salinity of Nakdong river estuary using deep learning algorithm (LSTM) for time series analysis, [Korea Literature], Journal of Korean Society of Coastal and Ocean Engineers, Vol. 34, No. 4, pp. 128-134DOI
39 
Wu J., Wang Z., 2022, A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory, Water, Vol. 14, No. 4, pp. 610DOI
40 
Xu J., Xu Z., Kuang J., Lin C., Xiao L., Huang X., Zhang Y., 2021, An alternative to laboratory testing: Random Forest-based water quality prediction framework for inland and nearshore water bodies, Water, Vol. 13, No. 22, pp. 3262DOI
41 
Yi H. S., Kim D. S., Hwang M. H., An K. G., 2016, Assessment of runoff and water temperature variations under RCP climate change scenario in Yongdam dam watershed, South Korea, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 32, No. 2, pp. 173-182DOI
42 
Yu S., Lee E., Park M., Kim K., Im J., Ryu I., Choi H., Byeon M., Noh H., 2018, Changes in the water environment based on the statistical data in the lake Paldang, [Korean Literature], Journal of Korean Society on Water Environment, Vol. 34, No. 6, pp. 688-702Google Search
43 
Zang C., Huang S., Wu M., Du S., Scholz M., Gao F., Lin C., Guo Y., Dong Y., 2010, Comparison of relationships between pH, dissolved oxygen and chlorophyll-a for aquaculture and non-aquaculture waters, Water, Air, & Soil Pollution, Vol. 219, No. 1, pp. 157-174DOI
44 
Zhang J. Y., Huang J., Yan F., Zhang Z. Q., 2009, Preliminary study on characters of dissolved oxygen and the relationship with pH in Meiliang lake, Journal of Fudan University, Vol. 48, No. 5, pp. 623-627Google Search
45 
Zhu S., Heddam S., 2020, Prediction of dissolved oxygen in urban rivers at the Three Gorges reservoir, China: Extreme learning machines (ELM) versus artificial neural network (ANN), Water Quality Research Journal, Vol. 55, No. 1, pp. 106-118DOI