JKSWE

1

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2

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Che X., Zhang H. K., Li Z. B., Wang Y., Sun Q., Luo D., Wang H., (2024), "Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 212, pp. 73-95.

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Chen J., Cao L., Cheng Y., Chen Z., Wang Z., Chen Y., Liu Z., Ma J., Xie P., (2024), "Efficient disinfection of combined sewer overflows by ultraviolet/peracetic acid through intracellular oxidation with preserving cell integrity," Water Research, vol. 260, pp. 121959.

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Chen Q., Al Kontar R., Nouiehed M., Yang X. J., Lester C., (2025), "Rethinking cost-sensitive classification in deep learning via adversarial data augmentation," INFORMS Journal on Data Science, vol. 4, no. 1, pp. 1-19.

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Chen S., Huang J., Huang J., Wang P., Sun C., Zhang Z., Jiang S., (2025), "Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms," Environmental Science and Ecotechnology, vol. 23, pp. 100522.

7

Chidiac S., El Najjar P., Ouaini N., El Rayess Y., El Azzi D., (2023), "A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives," Reviews in Environmental Science and Bio/Technology, vol. 22, no. 2, pp. 349-395.

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10

Fotias S. P., Gaganis V., (2023), "Workflow for predicting undersaturated oil viscosity using machine learning," Results in Engineering, vol. 20, pp. 101502.

11

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12

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14

Ghimire S., Deo R. C., Casillas-Pérez D., Salcedo-Sanz S., Acharya R., Dinh T., (2025), "Electricity demand uncertainty modeling with Temporal Convolution Neural Network models," Renewable and Sustainable Energy Reviews, vol. 209, pp. 115097.

15

Guniganti S. K., Regonda S. K., Rajagopalan B., (2026), "A hybrid hydrologic modeling framework-role of spatial resolution, calibration approaches and error modeling," Journal of Hydrology: Regional Studies, vol. 63, pp. 103053.

16

Houénafa S. E., Johnson O., Ronoh E. K., Moore S. E., (2025), "Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling," Results in Engineering, vol. 25, pp. 104079.

17

Kermorvant C., Liquet B., Litt G., Jones J. B., Mengersen K., Peterson E. E., Hyndman R. J., Leigh C., (2021), "Reconstructing missing and anomalous data collected from high-frequency in-situ sensors in fresh waters," International Journal of Environmental Research and Public Health, vol. 18, no. 23, pp. 12803.

18

Khorashadi Zadeh F., Nossent J., Woldegiorgis B. T., Bauwens W., Van Griensven A., (2022), "A fast and effective parameterization of water quality models," Environmental Modelling & Software, vol. 149, pp. 105331.

19

Kim J., Jones J. R., Seo D., (2021), "Factors affecting harmful algal bloom occurrence in a river with regulated hydrology," Journal of Hydrology: Regional Studies, vol. 33, pp. 100769.

20

Kim J., Park J., Oh H. J., (2026), "Optimal data pooling from multiple waterbodies to improve machine-learning predictions of cyanobacterial blooms," Journal of Contaminant Hydrology, vol. 276, pp. 104760.

21

Kim J., Park N. S., Yun S., Chae S. H., Yoon S., (2018), "Application of isolation forest technique for outlier detection in water quality data," Journal of Korean Society of Environmental Engineers, vol. 40, no. 12, pp. 473-480.[Korean Literature].

22

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.

23

Kim Y. J., Lee S. J., An K. G., (2019), "Characteristics of chemical water quality and the empirical model analysis before and after the construction of Baekje Weir," Environmental Biology Research, vol. 37, no. 1, pp. 48-59.[Korean Literature].

24

Kuo Y. M., Chen Y. H., Chiu Y. T., Lin T. F., (2025), "Water quality and physicochemical conditions drive chlorophyll-a concentrations in two connected subtropical off-stream reservoirs," Sustainable Environment Research, vol. 35, no. 1, pp. 15.

25

Lee G., Seo D., (2015), "Analysis on trends and major impact factors of water quality dynamics in the Gab-Cheon River, Daejeon, Korea," Journal of Korean Society of Environmental Engineers, vol. 37, no. 9, pp. 517-525.[Korean Literature].

26

Lee J., Yi C., Ryu J., Kim S., (2026), "Impact of oversampling on the predictive performance of machine learning models: Empirical analysis of algal bloom prediction," Journal of Korea Water Resources Association, vol. 59, no. 2, pp. 159-171.[Korean Literature].

27

Lee S. K., (2025), "Empirical analysis of XGBoost-based real estate automated valuation model," Journal of Real Estate Analysis, vol. 11, no. 2, pp. 21-40.[Korean Literature].

28

Liang T., Yin G., Zhao N., Jia R., Zhang X., Xu M., Zhang Z., Dong M., Hu X., Huang P., (2024), "Applying feature-similarity-metrics for long-tailed problem of phytoplankton microscopic images classification," Algal Research, vol. 82, pp. 103673.

29

Lin S., Pierson D. C., Mesman J. P., (2023), "Prediction of algal blooms via data-driven machine learning models: An evaluation using data from a well-monitored mesotrophic lake," Geoscientific Model Development, vol. 16, no. 1, pp. 35-46.

30

Liu C., Yuan X., Li H., Qi Y., Shen X., Miao S., Gao W., (2025), "DDWQI: A novel water quality index based on data-driven approaches," Ecological Indicators, vol. 178, pp. 113850.

31

Long J., Lu C., Lei Y., Chen Z. Y., Wang Y., (2025), "Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction," Scientific Reports, vol. 15, no. 1, pp. 12847.

32

Ly Q. V., Nguyen X. C., Lê N. C., Truong T.-D., Hoang T. H. T., Park T. J., Maqbool T., Pyo J., Cho K. H., Lee K. S., Hur J., (2021), "Application of machine learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea," Science of The Total Environment, vol. 797, pp. 149040.

33

Ma R., Ma Y., Liu X., (2025), "Time series anomaly detection via temporal relationship graphs and adaptive smoothing," Applied Soft Computing, vol. 179, pp. 113298.

34

Man G., Yun Q., Qilin Z., Yuyong L., Zhuoshi Z., (2025), "Surface water quality classification and prediction model based on multiple machine learning algorithms," Scientific Reports, vol. 15, no. 1, pp. 39907.

35

Martín-Suazo S., Morón-López J., Vakaruk S., Karamchandani A., Aguilar J. A. P., Mozo A., Gómez-Canaval S., Vinyals M., Ortiz J. M., (2024), "Deep learning methods for multi-horizon long-term forecasting of harmful algal blooms," Knowledge-Based Systems, vol. 301, pp. 112279.

36

Mowe M. A. D., Porojan C., Abbas F., Mitrovic S. M., Lim R. P., Furey A., Yeo D. C. J., (2015), "Rising temperatures may increase growth rates and microcystin production in tropical Microcystis species," Harmful Algae, vol. 50, pp. 88-98.

37

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38

Özdoğan-Sarıkoç G., Dadaser-Celik F., (2024), "Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin," Environmental Science and Pollution Research, vol. 31, no. 27, pp. 39098-39119.

39

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," Journal of the Korean Society of Water and Wastewater, vol. 36, no. 4, pp. 239-248.[Korean Literature].

40

Qian J., Qian L., Pu N., Bi Y., Wilhelms A., Norra S., (2024), "An intelligent early warning system for harmful algal blooms: Harnessing the power of big data and deep learning," Environmental Science & Technology, vol. 58, no. 35, pp. 15607-15618.

41

Ren H., Cromwell E., Kravitz B., Chen X., (2022), "Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks," Hydrology and Earth System Sciences, vol. 26, no. 7, pp. 1727-1743.

42

Serafin T., Weron R., (2025), "Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading," Energy Economics, vol. 148, pp. 108596.

43

Shi G., Nishizawa S., Matsushita T., Kato Y., Kozumi T., Matsui Y., Shirasaki N., (2021), "Computational fluid dynamics–based modeling and optimization of flow rate and radiant exitance for 1,4-dioxane degradation in a vacuum ultraviolet photoreactor," Water Research, vol. 197, pp. 117086.

44

Shiferaw N., Habte L., Waleed M., (2025), "Land use dynamics and their impact on hydrology and water quality of a river catchment: A comprehensive analysis and future scenario," Environmental Science and Pollution Research, vol. 32, no. 7, pp. 4124-4136.

45

Wang W., Wang G., Li J., Chen J., Gao Z., Fang L., Ren S., Wang Q., (2025), "Remote sensing identification and model-based prediction of harmful algal blooms in inland waters: Current insights and future perspectives," Water Research X, vol. 28, pp. 100369.

46

Waqas M., Humphries U. W., (2024), "A critical review of RNN and LSTM variants in hydrological time series predictions," MethodsX, vol. 13, pp. 102946.

47

Warren M. A., Simis S. G. H., Selmes N., (2021), "Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms," Remote Sensing of Environment, vol. 265, pp. 112651.

48

Wehrhahn C., Teufel H. J., (2010), "How Are Lateral Chromatic Interactions Computed from Cone Signals?," Neural Computation, vol. 22, no. 11, pp. 2763-2784.

49

Xu H., Wang X., Zhou Y., Xia T., Chang F. J., Xu C. Y., (2025), "Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model," Hydrology Research, vol. 56, no. 3, pp. 260-278.

50

Yang G., Blackburn J., She Y., Zhang W., (2025), "Winter water quality modelling under ice-affected conditions in rivers and lakes: A comprehensive review," Journal of Hydrology, vol. 657, pp. 133083.

51

Yang S., Yang D., Chen J., Santisirisomboon J., Lu W., Zhao B., (2020), "A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data," Journal of Hydrology, vol. 590, pp. 125206.

52

Yussof F. N., Maan N., Md Reba M. N., (2021), "LSTM networks to improve the prediction of harmful algal blooms in the West Coast of Sabah," International Journal of Environmental Research and Public Health, vol. 18, no. 14, pp. 7650.

53

Zhang W., Liu J., Xiao Y., Zhang Y., Yu Y., Zheng Z., Liu Y., Li Q., (2022), "The impact of cyanobacteria blooms on the aquatic environment and human health," Toxins, vol. 14, no. 10, pp. 658.

54

Zhang Y., Thorburn P. J., (2022), "Handling missing data in near real-time environmental monitoring: A system and a review of selected methods," Future Generation Computer Systems, vol. 128, pp. 63-72.

55

Zhao Z., Alzubaidi L., Zhang J., Duan Y., Gu Y., (2024), "A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations," Expert Systems with Applications, vol. 242, pp. 122807.

56

Zheng Y., Zhang X., Zhou Y., Zhang Y., Zhang T., Farmani R., (2025), "Deep representation learning enables cross-basin water quality prediction under data-scarce conditions," Npj Clean Water, vol. 8, no. 1, pp. 33.

57

Zhi W., Appling A. P., Golden H. E., Podgorski J., Li L., (2024), "Deep learning for water quality," Nature Water, vol. 2, no. 3, pp. 228-241.

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