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 
Ahmad A., Dey L., 2007, A k-mean clustering algorithm for mixed numeric and categorical data, Data & Knowledge Engineering, Vol. 63, pp. 503-527DOI
2 
Ayub J., Ahmad J., Muhammad J., Aziz L., Ayub S., Akram U., Basit I., 2016, Glaucoma detection through optic disc and cup segmentation using k-mean clustering, 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), pp. 143-147Google Search
3 
Bennett N. D., Croke B. F., Guariso G., Guillaume J. H., Hamilton S. H., Jakeman A. J., Marsili-Libelli S., Newham L. T., Norton J. P., Perrin C., 2013, Characterising performance of environmental models, Environmental Modelling & Software, Vol. 40, pp. 1-20DOI
4 
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 (KDD '16), Association for Computing Machinery, pp. 785-794Google Search
5 
Friedman J. H., 2001, Greedy function approximation: A gradient boosting machine, Annals of statistics, Vol. 29, No. 5, pp. 1189-1232Google Search
6 
Gray A. B., Pasternack G. B., Watson E. B., Goni M. A., Hatten J. A., Warrick J. A., 2016, Conversion to drip irrigated agriculture may offset historic anthropogenic and wildfire contributions to sediment production, Science of the Total Environment, Vol. 556, pp. 219-230Google Search
7 
Gray A. B., Pasternack G. B., Watson E. B., Warrick J. A., Goñi M. A., 2015, The effect of El Niño Southern Oscillation cycles on the decadal scale suspended sediment behavior of a coastal dry‐summer subtropical catchment, Earth Surface Processes and Landforms, Vol. 40, pp. 272-284Google Search
8 
Haghiabi A. H., Nasrolahi A. H., Parsaie A., 2018, Water quality prediction using machine learning methods, Water Quality Research Journal, Vol. 53, pp. 3-13DOI
9 
Hicks D. M., Gomez B., Trustrum N. A., 2000, Erosion thresholds and suspended sediment yields, Waipaoa river basin, New Zealand, Water Resources Research, Vol. 36, pp. 1129-1142DOI
10 
Hollister J. W., Milstead W. B., Kreakie B. J., 2016, Modeling lake trophic state: A random forest approach, Ecosphere, Vol. 7, pp. e01321Google Search
11 
Li L., Rong S., Wang R., Yu S., 2021, Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review, Chemical Engineering Journal, Vol. 405, pp. 126673Google Search
12 
Lin W., Sung S., Chen L., Chung H., Wang C., Wu R., Lee D., Huang C., Juang R., Peng X., 2004, Treating high-turbidity water using full-scale floc blanket clarifiers, Journal of Environmental Engineering, Vol. 130, No. 12, pp. 1481-1487DOI
13 
Moriasi D. N., Arnold J. G., Van Liew M. W., Bingner R. L., Harmel R. D., Veith T. L., 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the American Society of Agricultural and Biological Engineers, Vol. 50, No. 3, pp. 885-900Google Search
14 
Muhammad S. Y., Makhtar M., Rozaimee A., Aziz A. A., Jamal A. A., 2015, Classification model for water quality using machine learning techniques, International Journal of software engineering and its applications, Vol. 9, pp. 45-52DOI
15 
Packman A. I., MacKay J. S., 2003, Interplay of stream‐subsurface exchange, clay particle deposition, and streambed evolution, Water Resources Research, Vol. 39, No. 4, pp. 1097DOI
16 
Park J., 2021, Comparative characteristic of ensemble machine learning and deep learning models for turbidity prediction in a river, [Korean Literature], Journal of Korean Society of Water and Wastewater, Vol. 35, pp. 83-91DOI
17 
Park J., Hunt J. R., 2017, Coupling fine particle and bedload transport in gravel-bedded streams, Journal of Hydrology, Vol. 552, pp. 532-543DOI
18 
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, pp. 35-43DOI
19 
Park R. K., 2018, An empirical comparison and verification study on the containerports clustering measurement using k-means and hierarchical clustering (average linkage method Using Cross-Efficiency Metrics, and Ward Method) and Mixed Models, [Korean Literature], Journal of Korea Port Economic Association, Vol. 34, pp. 17-52DOI
20 
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., 2011, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, Vol. 12, pp. 2825-2830Google Search
21 
Shin Y., Kim T., Hong S., Lee S., Lee E., Hong S., Lee C., Kim T., Park M. S., Park J., 2020, Prediction of chlorophyll-a concentrations in the Nakdong river using machine learning methods, Water, Vol. 12, pp. 1822Google Search
22 
Singer M. B., Aalto R., James L. A., Kilham N. E., Higson J. L., Ghoshal S., 2013, Enduring legacy of a toxic fan via episodic redistribution of California gold mining debris, Proceedings of the National Academy of Sciences, Vol. 110, pp. 18436-18441DOI
23 
Song J., 2017, K-means cluster analysis for missing data, [Korean Literature], Journal of Korean Data Analysis Society, Vol. 19, pp. 689-697Google Search
24 
Stevenson M., Bravo C., 2019, Advanced turbidity prediction for operational water supply planning, Decision Support Systems, Vol. 119, pp. 72-84DOI
25 
Sutton C. D., 2005, Classification and regression trees, bagging, and boosting, Handbook of statistics, Vol. 24, pp. 303-329Google Search
26 
Uddameri V., Silva A. L. B., Singaraju S., Mohammadi G., Hernandez E. A., 2020, Tree-based modeling methods to predict nitrate exceedances in the Ogallala aquifer in Texas, Water, Vol. 12, pp. 1023Google Search
27 
United States Geological Survey (USGS), 2009, USGS(United States Geological Survey) Water-Data Report 2009, 11482500 Redwood Creek at Orick, CAGoogle Search
28 
United States Geological Survey (USGS), 2014, https://waterdata.usgs.gov/nwis (accessed Jun. 2014), National Water Information System (NWIS)
29 
Walling D., 1977, Assessing the accuracy of suspended sediment rating curves for a small basin, Water Resources Research, Vol. 13, No. 3, pp. 531-538DOI
30 
Wang Y., Chen J., Cai H., Yu Q., Zhou Z., 2021, Predicting water turbidity in a macro-tidal coastal bay using machine learning approaches, Estuarine, Coastal and Shelf Science, Vol. 252, pp. 107276DOI
31 
Warrick J. A., 2015, Trend analyses with river sediment rating curves, Hydrological processes, Vol. 29, No. 6, pp. 936-949DOI
32 
Warrick J. A., Madej M. A., Goñi M., Wheatcroft R., 2013, Trends in the suspended-sediment yields of coastal rivers of northern California, 1955–2010, Journal of Hydrology, Vol. 489, pp. 108-123DOI
33 
Zhang D., Qian L., Mao B., Huang C., Huang B., Si Y., 2018, A data-driven design for fault detection of wind turbines using random forests and XGboost, IEEE Access, Vol. 6, pp. 21020-21031Google Search
34 
Zhang Y., Bouadi T., Martin A., 2018, An empirical study to determine the optimal k in Ek-NNclus method, 5th International Conference on Belief Functions (BELIEF2018), pp. 260-268Google Search