과제정보
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (No. 2018R1A6A1A08025348).
참고문헌
- Aggarwal, C. C. (2018). Neural networks and deep learning, Springer. https://doi.org/10.1007/978-3-319-94463-0
- Bae, D., Shin, S. Y., Shim, J. H., Jeong, S. S., and Shin, S. Y. (2015). The 3rd forum on future policies civil engineering dealing with climate change and urbanization - Preparedness plan for disasters in urban areas, Journal of the Korean Society of civil engineers, 63(7), 36-51. [Korean Literature]
- Bernat-Quesada, F., Alvaro, M., Garcia, H., and Navalon, S. (2020). Impact of chlorination and pre-ozonation on disinfection by-products formation from aqueous suspensions of cyanobacteria: Microcystis aeruginosa, Anabaena aequalis and Oscillatoria tenuis, Water Research, 183, 116070. https://doi.org/10.1016/j.watres.2020.116070
- Cha, Y., Cho, K., Lee, H., Kang, T., and Kim, J. H. (2017). The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers, Water Research, 124, 11-19. https://doi.org/10.1016/j.watres.2017.07.040
- Cho, Y. M., Seo, Y., Maeng, S. K., and Hong, Y. (2020). Improving the safety of tap water in Gyeonggi-do, Gyeonggi Research Institute. [Korean Literature]
- Choi, S. Y., Han, K. Y., and Kim, B. H. (2012). Comparison of different multiple linear regression models for real-time flood stage forecasting, Journal of the Korean Society of Civil Engineers, 32(1B), 9-20. https://doi.org/10.12652/KSCE.2012.32.1B.009
- Health Canada. (2022). Guidelines for Canadian recreational water quality - Cyanobacteria and their Toxins, Health Canada.
- Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J. M. H., and Visser, P. M. (2018). Cyanobacterial blooms, Nature reviews, Microbiology, 16(8), 471-483. https://doi.org/10.1038/s41579-018-0040-1
- Intergovernmental Panel on Climate Change (IPCC). (2014). Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change, Core Writing Team, Pachauri, R. K. and Meyer, L. A. (eds.), Geneva, Switzerland, 151.
- Johnk, K. D., Huisman, J., Sharples, J., Sommeijer, B., Visser, P. M., and Stroom, J. M. (2008). Summer heatwaves promote blooms of harmful cyanobacteria, Global Change Biology, 14, 495-512. https://doi.org/10.1111/j.1365-2486.2007.01510.x
- Jung, W., Kim, S. E., and Kim, Y. D. (2021). Analysis of influential factors of cyanobacteria in the mainstream of Nakdong river using random forest, Journal of Wetlands Researh, 23(1), 27-34. [Korean Literature] https://doi.org/10.17663/JWR.2021.23.1.27
- Jung, Y. J. and Kim, Y. J. (2016). Relationship between energy consumption and operational variables at wastewater treatment plant, Journal of Korean Society on Water Environment, 32(3), 253-260. https://doi.org/10.15681/KSWE.2016.32.3.253
- Kim, H. G., Cha, Y., and Cho, K. H. (2024). Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency, Water Research, 249, 120928. https://doi.org/10.1016/j.watres.2023.120928
- Kim, H. G., Cho, K. H., and Recknagel, F. (2023). Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables, Water Research, 246, 120662. https://doi.org/10.1016/j.watres.2023.120662
- Kim, J., Jung, W., An, J., Oh, H. J., and Park, J. (2023). Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning, The Science of the Total Environment, 866, 161398. https://doi.org/https://doi.org/10.1016/j.scitotenv.2023.161398
- Lee, S., Choi, B., Kim, S. J., Kim, J., Kang, D., and Lee, J. (2022). Relationship between freshwater harmful algal blooms and neurodegenerative disease incidence rates in South Korea, Environmental Health, 21(1), 1-11. https://doi.org/10.1186/s12940-022-00935-y
- Li, Z., Chen, Q., Xu, Q., and Blanckaert, K. (2013). Generalized likelihood uncertainty estimation method in uncertainty analysis of numerical Eutrophication models: Take bloom as an example, Mathematical Problems in Engineering, 2013, 701923. https://doi.org/10.1155/2013/701923
- Ministry of Environment. (2020). Korean climate change assessment report 2020, Publish No. 11-1480000-001691-01, Ministry of Environment. [Korean Literature]
- Ministry of Environment. (2022). Annual report on algae (green algae) occurrence and response, Publish No. 11-14800000-001363-10, Ministry of Environment. [Korean Literature]
- Moon, H., Baik, J., Hwang, S., and Choi, M. (2014). Spatial downscaling of grid precipitation using support vector machine regression, Journal of Korea Water Resources Association, 47(11), 1095-1105. [Korean Literature] https://doi.org/10.3741/JKWRA.2014.47.11.1095
- National Disaster Management Research Institute. (2023). Disaster and safety system improvement report: Expansion of heat wave vulnerability management targets due to climate crisis, Publish No. 11-1741056-000568-01, National Disaster Management Research Institute. [Korean Literature]
- Paerl, H. W. and Huisman, J. (2008). Blooms like it hot, Science, 320(5872), 57-58. https://doi.org/10.1126/science.1155398
- Schneider, M. and Blaha, L. (2020). Advanced oxidation processes for the removal of cyanobacterial toxins from drinking water, Environmental Sciences Europe, 32(1), 94. https://doi.org/10.1186/s12302-020-00371-0
- United Stated Environmental Protection Agency (U. S. EPA.). (2019). Recommended human health recreational ambient water quality criteria or swimming advisories for microcystins and cylindrospermopsin, EPA 822-R-19-001. https://www.epa.gov/sites/default/files/2019-05/documents/hh-rec-criteria-habs-document-2019.pdf.
- Villanueva, P., Yang, J., Radmer, L., Liang, X., Leung, T., Ikuma, K., Swanner, E. D., Howe, A., and Lee, J. (2023). One-week-ahead prediction of cyanobacterial harmful algal blooms in Iowa lakes, Environmental Science & Technology, 57(49), 20636-20646. https://doi.org/10.1021/acs.est.3c07764
- Wisniewska, K., Lewandowska, A. U., and Sliwinska-Wilczewska, S. (2019). The importance of cyanobacteria and microalgae present in aerosols to human health and the environment-Review study, Environment International, 131, 104964. https://doi.org/10.1016/j.envint.2019.104964
- Woo, C. Y., Yun, S. L., Kim, S. K., and Lee, W. (2020). Occurrence of harmful blue-green algae at algae alert system and water quality forecast system sites in Daegu and Gyeongsangbuk-do between 2012 and 2019, Journal of Korean Society of Environmental Engineers, 42(12), 664-673. [Korean Literature] https://doi.org/10.4491/KSEE.2020.42.12.664
- World Health Organization (WHO). (2021). Guidelines on recreational water quality: Volume 1 Coastal and fresh waters, World Health Organization.
- Xiao, X., He, J., Huang, H., Miller, T. R., Christakos, G., Reichwaldt, E. S., Ghadouani, A., Lin, S., Xu, X., and Shi, J. (2017). A novel single-parameter approach for forecasting algal blooms, Water Research, 108, 222-231. https://doi.org/10.1016/j.watres.2016.10.076
- Xie, Z., Lou, I., Ung, W. K., and Mok, K. M. (2012). Freshwater algal bloom prediction by support vector machine in Macau storage reservoirs, Mathematical Problems in Engineering, 2012, 397473. https://doi.org/10.1155/2012/397473