과제정보
이 성과는 정부 (과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2022R1F1A1065518).
참고문헌
- Amorim, F. D. L. L. D., Rick, J., Lohmann, G., and Wiltshire, K. H. (2021). Evaluation of machine learning predictions of a highly resolved time series of chlorophyll-a concentration. Applied Sciences, 11(16), 7208.
- Barzegar, R., Aalami, M. T., and Adamowski, J. (2020). Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model. Stochastic Environmental Research and Risk Assessment, 34(2), 415-433.
- Blix, K., and Eltoft, T. (2018). Machine learning automatic model selection algorithm for oceanic chlorophyll-a content retrieval. Remote Sensing, 10(5), 775.
- Chauhan, K., Jani, S., Thakkar, D., Dave, R., Bhatia, J., Tanwar, S., and Obaidat, M. S. (2020). Automated machine learning: The new wave of machine learning. IEEE. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 205-212).
- Chen, C., Chen, Q., Yao, S., He, M., Zhang, J., Li, G., and Lin, Y. (2024). Combining physical-based model and machine learning to forecast chlorophyll-a concentration in freshwater lakes. Science of The Total Environment, 907, 168097.
- Chen, Y. W., Song, Q., and Hu, X. (2021). Techniques for automated machine learning. ACM SIGKDD Explorations Newsletter, 22(2), 35-50.
- Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F. (2015). Efficient and robust automated machine learning. Advances in neural information processing systems, 28.
- Hollmann, N., Muller, S., Eggensperger, K., and Hutter, F. (2022). Tabpfn: A transformer that solves small tabular classification problems in a second. arXiv preprint arXiv:2207.01848.
- Kim, H. R., Soh, H. Y., Kwak, M. T., and Han, S. H. (2022). Machine learning and multiple imputation approach to predict chlorophyll-a concentration in the coastal zone of Korea. Water, 14(12), 1862.
- Kim, K. M., and Ahn, J. H. (2022). Machine learning predictions of chlorophyll-a in the Han river basin, Korea. Journal of Environmental Management, 318, 115636.
- Kwon, Y. S., Baek, S. H., Lim, Y. K., Pyo, J., Ligaray, M., Park, Y., and Cho, K. H. (2018). Monitoring coastal chlorophyll-a concentrations in coastal areas using machine learning models. Water, 10(8), 1020.
- Li, H., Li, X., Song, D., Nie, J., and Liang, S. (2024). Prediction on daily spatial distribution of chlorophyll-a in coastal seas using a synthetic method of remote sensing, machine learning and numerical modeling. Science of The Total Environment, 910, 168642.
- Loftin, K. A., Graham, J. L., Hilborn, E. D., Lehmann, S. C., Meyer, M. T., Dietze, J. E., and Griffith, C. B. (2016). Cyanotoxins in inland lakes of the United States: Occurrence and potential recreational health risks in the EPA National Lakes Assessment 2007. Harmful algae, 56, 77-90.
- Magadan, L., Roldan-Gomez, J., Granda, J. C., & Suarez, F. J. (2023). Early fault classification in rotating machinery with limited data using TabPFN. IEEE Sensors Journal.
- Ministry of Environment (ME). (2023). The First Comprehensive Water Management Plan for the Geum River Basin, 2021-2030. Geum River Basin Management Commission pp 17-19
- Ministry of Environment (ME). (2024). Water Quality Monitoring Program. Ministry of Environment pp 6-7
- Moon, Y. H., Shin, I. H., Lee, Y. J., and Min, D. G. (2019). Recent research & development trends in automated machine learning. Electronics and Telecommunications Trends, 34(4), 32-42
- National Institute of Environmental Research (NIER). (2023). Water Environment Information System, https://water.nier.go.kr/web, Accessed 4 December 2023
- https:// water.nier.go.kr/web. Accessed 4 December 2023. P
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
- Schindler, D. W. (2006). Recent advances in the understanding and management of eutrophication. Limnology and oceanography, 51(1part2), 356-363.
- Shin, Y., Kim, T., Hong, S., Lee, S., Lee, E., Hong, S., ... and Heo, T. Y. (2020). Prediction of chlorophyll-a concentrations in the Nakdong River using machine learning methods. Water, 12(6), 1822.
- Tuggener, L., Amirian, M., Rombach, K., Lorwald, S., Varlet, A., Westermann, C., and Stadelmann, T. (2019). Automated machine learning in practice: state of the art and recent results. IEEE. In 2019 6th Swiss Conference on Data Science (SDS) (pp. 31-36).
- Wurtsbaugh, W. A., Paerl, H. W., and Dodds, W. K. (2019). Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wiley Interdisciplinary Reviews: Water, 6(5), e1373.