수질인자 머신러닝 분석을 통한 저수지 유해 남조류 발생예측

Breiman, L. (2001).Random forests. Machine learning, 45(1),pp. 5-32. https://doi.org/10.1023/A:1010933404324
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555.
Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kim S.-H., Park, J.H., Kim, B. (2021). Predication of Cyanobacteria Harmful Algal Blooms in Reservoir using Machine Learning and Deep Learning.Journal of Korea Water Resources Association, 54(spc1), pp. 1167-81. https://doi.org/10.3741/JKWRA.2021.54.S-1.1167
Liu, X., Lu, X., Chen, Y. (2011). The effects of temperature and nutrient ratios on Microcystis blooms in Lake Taihu, China an 11-year investigation. Harmful algae, 10(3), pp. 337-343. https://doi.org/10.1016/j.hal.2010.12.002