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2021 Vol.54, Issue 12S Preview Page

Research Article

31 December 2021. pp. 1167-1181
Abstract
References
1
Ahn, C.Y., Lee, C.S., Choi, J.W., Lee, S., and Oh, H.M. (2015). "Global occurrence of harmful cyanobacterial blooms and N, P-limitation strategy for bloom control." Korean Journal of Environmental Biology, Vol. 33, No. 1, pp. 1-6. (in Korean) 10.11626/KJEB.2015.33.1.001
2
Breiman, L. (2001). "Random forests." Machine learning, Vol. 45, No. 1, pp. 5-32. 10.1023/A:1010933404324
3
Cha, Y., Shin, J., and Kim, Y. (2020). "Data-driven modeling of freshwater aquatic systems: Status and prospects." Journal of Korean Society on Water Environment, Vol. 36, No. 6, pp. 611-620. (in Korean) 10.15681/KSWE.2020.36.6.611
4
Chollet, F. (2018). Deep learning with Python (Vol. 361). Manning, NY, U.S., pp. 28-47.
5
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint, arXiv1412.3555.
6
Dale, D. (2017). Summing feature importance in Scikit-learn for a set of features, accessed 10 October 2021, <http://bit.ly/2TYUjnu>.
7
Derot, J., Yajima, H., and Jacquet, S. (2020). "Advances in forecasting harmful algal blooms using machine learning models a case study with Planktothrix rubescens in Lake Geneva." Harmful Algae, Vol. 99, 101906. 10.1016/j.hal.2020.10190633218452
8
Evans, J.D. (1996). Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co., Pacific Grove, CA, U.S.
9
Guzel, H.O. (2019). Prediction of freshwater harmful algal blooms in Western Lake Erie using artificial neural network modeling techniques. Master Thesis, North Dakota State University, U.S.
10
Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. 10.1162/neco.1997.9.8.17359377276
11
Hong, H.W., Jo, E.S., Kang, S.A., and Han, K.J. (2020). Development and application of algae bloom phenomenon prediction technology using artificial intelligence deep learning. Korea Environmental Institute. (in Korean)
12
Huang, J., Zheng, H., Wang, H., and Jiang, X. (2017). "Machine learning approaches for cyanobacteria bloom prediction using metagenomic sequence data, a case study." 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) IEEE, Kansas City, MO, U.S., pp. 2054-2061. 10.1109/BIBM.2017.8217977
13
Jung, K.W., Yoon, C.G., Jang, J.H., and Jeon, J.H. (2006). "Water quality and correlation analysis between water quality parameters in the Hwaong watershed." Journal of The Korean Society of Agricultural Engineers, Vol. 48, No. 1. (in Korean) 10.5389/KSAE.2006.48.1.091
14
Jung, W.S., Jo, B.G., Kim, Y.D., and Kim, S.E. (2019). "A study on the characteristics of cyanobacteria in the mainstream of Nakdong river using decision trees." Journal of Wetlands Research, Vol. 21, No. 4, pp. 312-320. (in Korean)
15
Korea Environmental Institute (KEI) (2018). Environmental big data analysis and service development (Ⅱ). (in Korean)
16
K-water (2010). Bohyeonsan multipurpose dam construction project detailed design report. (in Korean)
17
K-water (2019). Bohyeonsan dam basin pollution source detailed investigation report. (in Korean)
18
Lee, H.M., Shin, R.Y., Lee, J.H., and Park, J.G. (2019). "A study on the relationship between cyanobacteria and environmental factors in Yeongcheon Lake." Journal of Korean Society on Water Environment, Vol. 35, No. 4, pp. 352-361. (in Korean)
19
Liaw, A., and Wiener, M. (2002). "Classification and regression by randomForest." R News, Vol. 2, No. 3, pp. 18-22.
20
Liu, X., Lu, X., and Chen, Y. (2011). "The effects of temperature and nutrient ratios on microcystis blooms in Lake Taihu, China an 11-year investigation." Harmful Algae, Vol. 10, No. 3, pp. 337-343. 10.1016/j.hal.2010.12.002
21
McCulloch, W.S., and Pitts, W. (1943). "A logical calculus of the ideas immanent in nervous activity." The Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133. 10.1007/BF02478259
22
Mitchell, A.W., Bramley, R.G.V., and Johnson, A.K.L. (1997). "Export of nutrients and suspended sediment during a cyclone-mediated flood event in the Herbert River catchment, Australia." Marine and Freshwater Research, Vol. 48, No. 1, pp. 79-88. 10.1071/MF96021
23
National Information Society Agency (NIA) (2017). Development of machine learning-based algal bloom prediction model. (in Korean)
24
National Institute of Environmental Research (NIER) (2011). A study on early forecasting for algal blooms using artificial neural networks (II). (in Korean)
25
National Institute of Environmental Research (NIER) (2018). A study on the characteristics of algae depending rivers and lake (I). (in Korean)
26
National Institute of Environmental Research (NIER) (2021). Water environment information system, accessed 13 September 2021, <http://water.nier.go.kr/>.
27
Park, J., Moon, M., Lee, H., and Kim, K. (2014). "A study on characteristics of water quality using multivariate analysis in Sumjin River basin." Journal of Korean Society on Water Environment, Vol. 30, No. 2, pp. 119-127. (in Korean) 10.15681/KSWE.2014.30.2.119
28
Shin, Y., Kim, T., Hong, S., Lee, S., Lee, E., Hong, S., Lee, C., Kim, T., Park M., Park J., and Heo, T.Y. (2020). "Prediction of chlorophyll-a concentrations in the Nakdong River using machine learning methods." Water, Vol. 12, No. 6, 1822. 10.3390/w12061822
29
Song, E.S., Cho, K.A., and Shin, Y.S. (2015). "Exploring the dynamics of dissolved oxygen and vertical density structure of water column in the Youngsan Lake." Journal of Environmental Science International, Vol. 24, No. 2, pp. 163-174. (in Korean) 10.5322/JESI.2015.24.2.163
30
Tayfur, G. (2014). Soft computing in water resources engineering Artificial neural networks, fuzzy logic and genetic algorithms. WIT Press, Southampton, UK.
31
Water Resources Management Information System (WAMIS) (2021). Republic of Korea, accessed 10 September 2021, <http://www. wamis.go.kr/>.
32
Weiss, R.F. (1970). "The solubility of nitrogen, oxygen and argon in water and seawater." Deep Sea Research and Oceanographic Abstracts, Vol. 17, No. 4, pp. 721-735. 10.1016/0011-7471(70)90037-9
33
Yi, H.S., Park, S., An, K.G., and Kwak, K.C. (2018). "Algal bloom prediction using extreme learning machine models at artificial weirs in the Nakdong River, Korea." International Journal of Environmental Research and Public Health, Vol. 15, No. 10, p. 2078. 10.3390/ijerph1510207830248912PMC6210959
34
Yu, P., Gao, R., Zhang, D., and Liu, Z.P. (2021). "Predicting coastal algal blooms with environmental factors by machine learning methods." Ecological Indicators, Vol. 123, 107334. 10.1016/j.ecolind.2020.107334
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 54
  • No :12
  • Pages :1167-1181
  • Received Date :2021. 09. 30
  • Revised Date :2021. 11. 17
  • Accepted Date : 2021. 11. 21