JKSWE

1

Ali A. J., Ahmed A. A., (2026), "Advancing river water quality prediction: A comparative assessment of deep learning models for dissolved oxygen forecasting," International Journal of River Basin Management, pp. 1-22.

2

Allahyari S., Hosseinalipour S. M., Asiaei S., (2025), "Deep learning approaches for predicting solar radiation and freshwater yield in modified pyramid solar still," Scientific Reports, vol. 15, no. 1, pp. 41322.

3

An M., Liu Q., Wang Z., Chen Y., (2026), "Fusing multi-dimensional features with heterogeneous deep learning for robust dissolved oxygen prediction and control-oriented regulation analysis in complex lakes," Water Research X, vol. 30, pp. 100501.

4

Anderson D. M., Wells M. L., Trainer V. L., Suddleson M., Claridge K., Coyne K. J., Dortch Q., Gobler C. J., Heil C. A., Inaba N., Laughinghouse H. D., Mardones J. I., Nakayama N., Park T., Peacock M. B., Pokrzywinski K., Raymond H., Toyoda J. H., Trethewey D., Visser P. M., Wang Y., Yuan Y., (2025), "Controlling harmful algal blooms (HABs) in marine waters: Review of current status and future prospects," Harmful Algae, vol. 150, pp. 102989.

5

Boyer J. N., Kelble C. R., Ortner P. B., Rudnick D. T., (2009), "Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA," Ecological Indicators, vol. 9, no. 6, pp. S56-S67.

6

Busari I., Sahoo D., Das N., Privette C., Schlautman M., Sawyer C., (2025), "Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method," Journal of Environmental Management, vol. 382, pp. 125441.

7

Cao H., Xie X., Xiao Z., Liu W., (2024), "Transferability of machine learning models for geogenic contaminated groundwaters," Environmental Science & Technology, vol. 58, no. 20, pp. 8783-8791.

8

Chang C. C., Chen Y., Chen C. Y., (2026), "Towards decision-ready explainable machine learning for water quality management using a consistency index and cross-validated feature-selection framework," Environmental Research, vol. 297, pp. 124207.

9

Chang F. C., Garcia V., Tang C., Vlahos P., Wanik D., Yan J., Bash J. O., Astitha M., (2021), "Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations," Journal of Great Lakes Research, vol. 47, no. 6, pp. 1656-1670.

10

Chen D., Yan X., Liu X., Li S., Wang L., Tian X., (2021), "A multiscale-grid-based stacked bidirectional GRU neural network model for predicting traffic speeds of urban expressways," IEEE Access, vol. 9, pp. 1321-1337.

11

Chen X., Sun W., Jiang T., Ju H., (2024), "Enhanced prediction of river dissolved oxygen through feature- and model-based transfer learning," Journal of Environmental Management, vol. 372, pp. 123310.

12

Cinar Y. G., Mirisaee H., Goswami P., Gaussier E., Aït-Bachir A., (2018), "Period-aware content attention RNNs for time series forecasting with missing values," Neurocomputing, vol. 312, pp. 177-186.

13

Devika A. K., Sanodiya R. K., Jose B. R., Mathew J., (2023), "Visual domain adaptation through locality information," Engineering Applications of Artificial Intelligence, vol. 123, no. Part A, pp. 106172.

14

Dong J., Jiang Y., Chen P., Li J., Wang Z., Han S., (2025), "Short-term power load forecasting using bidirectional gated recurrent units-based adaptive stacked autoencoder," International Journal of Electrical Power & Energy Systems, vol. 165, pp. 110459.

15

Fournier C., Fernandez-Fernandez R., Cirés S., López-Orozco J. A., Besada-Portas E., Quesada A., (2024), "LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data," Water Research, vol. 267, pp. 122553.

16

Frischmon C., Silberstein J., Guth A., Mattson E., Porter J., Hannigan M., (2025), "Improving the quantification of peak concentrations for air quality sensors via data weighting," Atmospheric Measurement Techniques, vol. 18, no. 13, pp. 3147-3159.

17

Gupta S., Gupta S., (2025), "Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay," Scientific Reports, vol. 15, no. 1, pp. 30877.

18

Hakami A., (2024), "Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance," Scientific Reports, vol. 14, no. 1, pp. 9645.

19

Heisler J., Glibert P. M., Burkholder J. M., Anderson D. M., Cochlan W., Dennison W. C., Dortch Q., Gobler C. J., Heil C. A., Humphries E., Lewitus A., Magnien R., Marshall H. G., Sellner K., Stockwell D. A., Stoecker D. K., Suddleson M., (2008), "Eutrophication and harmful algal blooms: A scientific consensus," Harmful Algae, vol. 8, no. 1, pp. 3-13.

20

Hridoy Md. A. A. M., Shawkat A. I., Bordin C., Acharjee M. R., Masood A., Baki A. O., Al Mamun Md. A., (2025), "Advanced machine learning models for accurate water quality classification and WQI prediction: Implications for aquatic disease risk management," Science of The Total Environment, vol. 1008, pp. 180965.

21

Huan J., Liao W., Zheng Y., Xu X., Zhang H., Shi B., (2023), "A deep learning model with spatio-temporal graph convolutional networks for river water quality prediction," Water Supply, vol. 23, no. 7, pp. 2940-2957.

22

Jeong B., Chapeta M. R., Kim M., Kim J., Shin J., Cha Y., (2022), "Machine learning-based prediction of harmful algal blooms in water supply reservoirs," Water Quality Research Journal, vol. 57, no. 4, pp. 304-318.

23

Khoshvaght H., Permala R. R., Razmjou A., Khiadani M., (2025), "A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction," Journal of Environmental Chemical Engineering, vol. 13, no. 6, pp. 119675.

24

Kim D., Chung S., (2025), "Enhancing harmful algal bloom predictions through integrated modeling of turbidity and nutrient dynamics in monsoon climate reservoirs," Journal of Environmental Management, vol. 381, pp. 125291.

25

Kim H. G., Hong S., Chon T. S., Joo G. J., (2021), "Spatial patterning of chlorophyll a and water-quality measurements for determining environmental thresholds for local eutrophication in the Nakdong River basin," Environmental Pollution, vol. 268, pp. 115701.

26

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.

27

Lee Y. S., Yen S. J., Jiang W., Chen J., Chang C. Y., (2025), "Illuminating the black box: An interpretable machine learning based on ensemble trees," Expert Systems with Applications, vol. 272, pp. 126720.

28

Liao Z., Wang X., Tian W., Xie W., (2025), "Enhancing surface water quality prediction in data-scarce sites using transfer learning and neural networks," Journal of Water Process Engineering, vol. 75, pp. 107923.

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

Ma C., Sun W., Yang Z., Wang J., Zhou L., (2025), "Spatiotemporal variations in land use impacts on river water quality in a mountain-to-plain transitional basin in arid region of northern China," Journal of Contaminant Hydrology, vol. 271, pp. 104542.

31

Ma K., Feng D., Lawson K., Tsai W., Liang C., Huang X., Sharma A., Shen C., (2021), "Transferring hydrologic data across continents – Leveraging data-rich regions to improve hydrologic prediction in data-sparse regions," Water Resources Research, vol. 57, no. 5, pp. e2020WR028600.

32

Makumbura R. K., Mampitiya L., Rathnayake N., Meddage D. P. P., Henna S., Dang T. L., Hoshino Y., Rathnayake U., (2024), "Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature," Results in Engineering, vol. 23, pp. 102831.

33

National Institute of Environmental Research, (2026), "Water Environment Information System: Automatic Monitoring Network Data Inquiry."

34

Regazzoni F., Pagani S., Salvador M., Dede’ L., Quarteroni A., (2024), "Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks," Nature Communications, vol. 15, no. 1, pp. 1834.

35

Reich N. G., Lessler J., Sakrejda K., Lauer S. A., Iamsirithaworn S., Cummings D. A. T., (2016), "Case study in evaluating time series prediction models using the relative mean absolute error," The American Statistician, vol. 70, no. 3, pp. 285-292.

36

Talagala P. D., Hyndman R. J., Leigh C., Mengersen K., Smith‐Miles K., (2019), "A feature-based procedure for detecting technical outliers in water-quality data from in situ sensors," Water Resources Research, vol. 55, no. 11, pp. 8547-8568.

37

Tian W., Liao Z., Wang X., (2019), "Transfer learning for neural network model in chlorophyll-a dynamics prediction," Environmental Science and Pollution Research, vol. 26, no. 29, pp. 29857-29871.

38

Vikram S., Collier-Oxandale A., Ostertag M. H., Menarini M., Chermak C., Dasgupta S., Rosing T., Hannigan M., Griswold W. G., (2019), "Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring," Atmospheric Measurement Techniques, vol. 12, no. 8, pp. 4211-4239.

39

Wai K. P., Koo C. H., Huang Y. F., Chong W. C., El-Shafie A., Sherif M., Ahmed A. N., (2025), "A practical temporal transfer learning model for multi- step water quality index forecasting using A CNN-coupled dual-path LSTM network," Journal of Hydrology: Regional Studies, vol. 60, pp. 102553.

40

Wang D., Li L., Ning R., Shao Y., Li H., Shi X., Xue Z., Togbah C. F., Yu S., Gao N., (2024), "Satellite tracking reveals the speed up of the lacustrine algal bloom drift in response to climate change," Environmental Science & Technology, vol. 58, no. 26, pp. 11727-11736.

41

Wang Z., Osmond P., Shi B., Janmohammadi M., Zhang K., (2026), "Next-generation water quality monitoring: sensor-based deep learning prediction and calibration optimization in urban rivers," Journal of Hydrology, vol. 666, pp. 134715.

42

Weber N., Bieroza M., (2026), "The impact of data quality and outlier detection in high-frequency water quality data on water management and process understanding," Journal of Hydrology, vol. 670, pp. 135171.

43

Xiao J., Li S., Tian Y., Huang J., Jiang X., Wang S., (2025), "Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring," Scientific Reports, vol. 15, no. 1, pp. 6000.

44

Yan J., Chen X., Zhou L., Liu W., Mahmood R., Hang X., (2025), "Spatial transferability of machine learning models for water quality prediction: A progressive data integration approach," Journal of Water Process Engineering, vol. 80, pp. 109230.

45

Yang X., Wu X., Hao H., He Z., (2008), "Mechanisms and assessment of water eutrophication," Journal of Zhejiang University SCIENCE B, vol. 9, no. 3, pp. 197-209.

46

Zhang X., Chen X., Zheng G., Cao G., (2023), "Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition," Environmental Research, vol. 221, pp. 115259.

47

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.

JKSWE The Journal of
the Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal ofthe Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal of
the Korean Society on Water Environment