Browse > Article
http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1167

Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning  

Kim, Sang-Hoon (Bohyunsan Dam Office of K-water)
Park, Jun Hyung (National Civil Defence and Disaster Management Training Institute, Ministry of the Interior and Safety)
Kim, Byunghyun (Department of Civil Engineering, Kyungpook National University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1167-1181 More about this Journal
Abstract
In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.
Keywords
Algae; Cyanobacteria; Machine learning; Deep learning; Water temperature; Random forest;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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)   DOI
2 Dale, D. (2017). Summing feature importance in Scikit-learn for a set of features, accessed 10 October 2021, .
3 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.
4 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.
5 Korea Environmental Institute (KEI) (2018). Environmental big data analysis and service development (II). (in Korean)
6 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)   DOI
7 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)   DOI
8 Tayfur, G. (2014). Soft computing in water resources engineering Artificial neural networks, fuzzy logic and genetic algorithms. WIT Press, Southampton, UK.
9 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.   DOI
10 National Institute of Environmental Research (NIER) (2011). A study on early forecasting for algal blooms using artificial neural networks (II). (in Korean)
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 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)   DOI
13 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.   DOI
14 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.   DOI
15 Evans, J.D. (1996). Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co., Pacific Grove, CA, U.S.
16 Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
17 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)   DOI
18 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)
19 K-water (2010). Bohyeonsan multipurpose dam construction project detailed design report. (in Korean)
20 K-water (2019). Bohyeonsan dam basin pollution source detailed investigation report. (in Korean)
21 Liaw, A., and Wiener, M. (2002). "Classification and regression by randomForest." R News, Vol. 2, No. 3, pp. 18-22.
22 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.   DOI
23 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.   DOI
24 National Information Society Agency (NIA) (2017). Development of machine learning-based algal bloom prediction model. (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, .
27 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.   DOI
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.   DOI
29 Water Resources Management Information System (WAMIS) (2021). Republic of Korea, accessed 10 September 2021, .
30 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.   DOI
31 Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint, arXiv1412.3555.
32 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)   DOI
33 Breiman, L. (2001). "Random forests." Machine learning, Vol. 45, No. 1, pp. 5-32.   DOI
34 Chollet, F. (2018). Deep learning with Python (Vol. 361). Manning, NY, U.S., pp. 28-47.