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NTIS 바로가기생태와 환경 = Korean journal of ecology and environment, v.56 no.3, 2023년, pp.268 - 279
박용은 (건국대학교 사회환경공학부) , 김진휘 (건국대학교 사회환경공학부) , 이한규 (건국대학교 사회환경플랜트공학과) , 변서현 (건국대학교 사회환경플랜트공학과) , 황순진 (건국대학교 환경보건과학과) , 신재기 (수생태원 한강 (韓江))
Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a ...
Al-Sulttani, A.O., M. Al-Mukhtar, A.B. Roomi, A.A. Farooque,?K.M. Khedher and Z.M. Yaseen. 2021. Proposition of new?ensemble data-intelligence models for surface water quality prediction. IEEE (Institute of Electrical and Electronics Engineers) Access 9: 108527-108541.
Anderson, D.M., A.D. Cembella and G.M. Hallegraeff. 2012.?Progress in understanding harmful algal blooms: paradigm?shifts and new technologies for research, monitoring, and?management. Annual Review of Marine Science 3: 143-176.
Baker, R.E., J.M. Pena, J. Jayamohan and A. Jerusalem. 2018.?Mechanistic models versus machine learning, a fight worth?fighting for the biological community? Biology Letters 14: 20170660.
Bertone, E., M.A. Burford and D.P. Hamilton. 2018. Fluorescence?probes for real-time remote cyanobacteria monitoring: a?review of challenges and opportunities. Water Research?141: 152-162.
Bruder, S., M. Babbar-Sebens, L. Tedesco and E. Soyeux. 2014.?Use of fuzzy logic models for prediction of taste and odor?compounds in algal bloom-affected inland water bodies.?Environmental Monitoring Assessment 186: 1525-1545.
Cawley, G.C. and N.L. Talbot. 2010. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal Machine Learning Research 11: 2079-2107.
Chen, C., J.C. Huang, Q.W. Chen, J.Y. Zhang, Z.J. Li and Y.Q. Lin. 2019. Assimilating multi-source data into a three-dimensional hydro-ecological dynamics model using Ensemble?Kalman Filter. Environmental Modelling and Software 117:?188-199.
Chen, Q.W. and A.E. Mynett. 2003. Integration of data mining?techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake. Ecological Modelling?162: 55-67.
Dodds, W.K., W.W. Bouska, J.L. Eitzmann, T.J. Pilger, K.L. Pitts,?A.J. Riley, J.T. Schloesser and D.J. Thornbrugh. 2009.?Eutrophication of U.S. freshwaters: Analysis of potential?economic damages. Environmental Science and Technology?43: 12-19.
Fornarelli, R., S. Galelli, A. Castelletti, J.P. Antenucci and C.L.?Marti. 2013. An empirical modeling approach to predict and?understand phytoplankton dynamics in a reservoir affected?by interbasin water transfers. Water Resources Research?49: 3626-3641.
Gardner, R.C. 2000. Correlation, causation, motivation, and second language acquisition. Canadian Psychology/Psychologie Canadienne 41: 10-24.
Gelman, A. and J. Hill. 2006. Data Analysis Using Regression?and Multilevel/Hierarchical Models. Cambridge University?Press, Cambridge, England. 648p.
Guven, B. and A. Howard. 2006. A review and classification of?the existing models of cyanobacteria. Progress in Physical?Geography: Earth and Environment 30: 1-24.
Hamilton, D.P., K.R. O'Brien, M.A. Burford, J.D. Brookes and?C.G. McBride. 2010. Vertical distributions of chlorophyll?in deep, warm monomictic lakes. Aquatic Sciences 72: 295-307.
Harada, M., T. Tominaga, K. Hiramatsu and A. Marui. 2013. Real-time prediction of chlorophyll-a time series in a eutrophic?agricultural reservoir in a coastal zone using recurrent neural networks with periodic chaos neurons. Irrigation and?Drainage 62: 36-43.
Harris, T.D. and J.L. Graham. 2017. Predicting cyanobacterial?abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake and Reservoir Management 33: 32-48.
Hwang, S.J., K. Kim, C. Park, W. Seo, B.G. Choi, H.S. Eum, M.H.?Park, H.R. Noh, Y.B. Sim and J.K. Shin. 2016. Hydro-meteorological effects on water quality variability in Paldang Reservoir, confluent area of the South-Han River-North-Han River-Gyeongan Stream, Korea. Korean Journal?of Ecology and Environment 49: 354-374.
Hwang, S.J., Y.B. Sim, B.G. Choi, K. Kim, C. Park, W. Seo, M.H.?Park, S.W. Lee and J.K. Shin. 2017. Rainfall and hydrological comparative analysis of water quality variability in?Euiam Reservoir, the North-Han River, Korea. Korean Journal of Ecology and Environment 50: 29-45.
Kim, S.H., J.H. Park and B. Kim. 2021. Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning. Journal of Korea Water Resources?Association 54: 1167-1181.
Kratzert, F., D. Klotz, M. Herrnegger, A.K. Sampson, S. Hochreiter and G.S. Nearing. 2019. Toward improved predictions in ungauged basins: Exploiting the power of machine?learning. Water Resources Research 55: 11344-11354.
LeCun, Y., Y. Bengio and G. Hinton. 2015. Deep learning. Nature?521: 436-444.
Lee, E., E.H. Na and K. Kim. 2012. The establishment of water?quality forecasting system for preemptive water quality?management. Rural Resources 54: 50-55.
Liu, Y., Z. Wang, H. Guo, S. Yu and H. Sheng. 2013. Modelling?the effect of weather conditions on cyanobacterial bloom?outbreaks in Lake Dianchi: a rough decision-adjusted?logistic regression model. Environmental Modeling and?Assessment 18: 199-207.
Luo, Y., K. Yang, Z.Y. Yu, J.Y. Chen, Y.F. Xu, X.L. Zhou and Y.?Yang. 2017. Dynamic monitoring and prediction of Dianchi?Lake cyanobacteria outbreaks in the context of rapid urbanization. Environmental Science and Pollution Research 24:?5335-5348.
Maier, H.R. and G.C. Dandy. 2000. Neural networks for the?prediction and forecasting of water resources variables: a?review of modelling issues and applications. Environmental?Modelling and Software 15: 101-124.
Millie, D.F., G.R. Weckman, G.L. Fahnenstiel, H.J. Carrick, E.?Ardjmand, W.A. Young II, M.J. Sayers and R.A. Shuchman. 2014. Using artificial intelligence for cyanoHAB?niche modeling: discovery and visualization of Microcystis-environmental associations within western Lake Erie.?Canadian Journal of Fisheries and Aquatic Sciences 71: 1642-1654.
Ministry of Environment-National Institute of Environmental?Research (MOE-NIER). 2020. A Manual of Algal Alert?System. NIER-GP2020-019. Incheon, Republic of Korea.
Mitrovic, S.M., L. Hardwick and F. Dorani. 2010. Use of flow?management to mitigate cyanobacterial blooms in the Lower?Darling River, Australia. Journal of Plankton Research 33:?229-241.
Moe, S.J., S. Haande and R.M. Couture. 2016. Climate change,?cyanobacteria blooms and ecological status of lakes: a?Bayesian network approach. Ecological Modelling 337:?330-347.
Mooij, W.M., D. Trolle, E. Jeppesen, G. Arhonditsis, P.V. Belolipetsky, D.B.R. Chitamwebwa, A.G. Degermendzhy, D,L.?DeAngelis, L.N.D. Domis, A.S. Downing, J.A. Elliott, C.R.?Fragoso, U. Gaedke, S.N. Genova, R.D. Gulati, L. Hakanson, D.P. Hamilton, M.R. Hipsey, J. 't Hoen, S. Hulsmann,?F.H. Los, V. Makler-Pick, T. Petzoldt, I.G. Prokopkin, K.?Rinke, S.A. Schep, K. Tominaga, A.A. van Dam, E.H. van?Nes, S.A. Wells and J.H. Janse. 2010. Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquatic Ecology 44: 633-667.
Nichols, S., R. Norris, W. Maher and M. Thoms. 2006. Ecological?effects of serial impoundment on the Cotter River, Australia.?Hydrobiologia 572: 255-273.
O'Hara, R.B. and D.J. Kotze. 2010. Do not log-transform count?data. Methods in Ecology and Evolution 1: 118-122.
Office of Science and Technology Policy (OSTP). 2016. Harmful?Algal Blooms and Hypoxia Comprehensive Research Plan?and Action Strategy: An Interagency Report. National?Science and Technology Council Subcommittee on Ocean?Science and Technology, USA. 94p.
Ostfeld, A., A. Tubaltzev, M. Rom, L. Kronaveter, T. Zohary and?G. Gal. 2015. Coupled data-driven evolutionary algorithm?for toxic cyanobacteria (blue-green algae) forecasting in?Lake Kinneret. Journal of Water Resources Planning and?Management 141: 04014069-13
Paerl, H.W. 2014. Mitigating harmful cyanobacterial blooms in a?human- and climatically-impacted World. Life 4: 988-1012.
Paerl, H.W. and D.F. Millie. 1996. Physiological ecology of toxic?aquatic cyanobacteria. Phycologia 35: 160-167.
Paerl, H.W. and J. Huisman. 2009. Climate change: a catalyst for?global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports 1: 27-37.
Paerl, H.W. and T.G. Otten. 2013. Harmful cyanobacterial blooms:?causes, consequences and controls. Microbial Ecology 65:?995-1010.
Page, T., P.J. Smith, K.J. Beven, I.D. Jones, J.A. Elliott, S.C.?Maberly, E.B. Mackay, M. De Ville and H. Feuchtmayr. 2018. Adaptive forecasting of phytoplankton communities.?Water Research 134: 74-85.
Peters, D.P., K.M. Havstad, J. Cushing, C. Tweedie, O. Fuenres?and N. Villanueva-Rosales. 2014. Harnessing the power?of big data: Infusing the scientific method with machine?learning to transform ecology. Ecosphere 5: 1-15.
Qin, B., J. Deng, K. Shi, J. Wang, J. Brookes, J. Zhou, Y. Zhang,?G. Zhu, H.W. Pearl and L. Wu. 2021. Extreme climate anomalies enhancing cyanobacterial blooms in eutrophic Lake Taihu, China. Water Resources Research 57: e2020WR029371.
Qin, B., W. Li, G. Zhu, Y. Zhang, T. Wu and G. Gao. 2015. Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu?(China). Journal of Hazardous Materials 287: 356-363.
Raps, S., K. Wyman, H.W. Siegelman and P.G. Falkowski. 1983.?Adaptation of the cyanobacterium Microcystis aeruginosa?to light intensity. Plant Physiology 72: 829-832.
Recknagel, F., M. French, P. Harkonen and K.I. Yabunaka. 1997.?Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling 96: 11-28.
Recknagel, F., P.T. Orr and H.Q. Cao. 2014. Inductive reasoning?and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation. Harmful Algae 31: 26-34.
Recknagel, F., P.T. Orr, M. Bartkow, A. Swanepoel and H. Cao. 2017. Early warning of limit-exceeding concentrations of?cyanobacteria and cyanotoxins in drinking water reservoirs?by inferential modelling. Harmful Algae 69: 18-27.
Recknagel, F., T. Fukushima, T. Hanazato, N. Takamura and H.?Wilson. 1998. Modelling and prediction of phyto- and?zooplankton dynamics in Lake Kasumigaura by artificial?neural networks. Lakes and Reservoirs: Research and?Management 3: 123-133.
Reynolds, C.S. and A.E. Walsby. 1975. Water-blooms. Biological?Reviews 50: 437-481.
Reynolds, C.S., R.L. Oliver and A.E. Walsby. 1987. Cyanobacterial dominance: the role of buoyancy regulation in dynamic?lake environments. New Zealand Journal of Marine and?Freshwater Research 21: 379-390.
Rousso, B.Z., E. Bertone, R. Stewart and D.P. Hamilton. 2020. A?systematic literature review of forecasting and predictive?models for cyanobacteria blooms in freshwater lakes. Water?Research 182: 115959.
Schindler, D.W. 2012. The dilemma of controlling cultural eutrophication of lakes. Proceedings of The Royal Society B 279:?4322-4333.
Schuwirth, N., F. Borgwardt, S. Domisch, M. Friedrichs, M.?Kattwinkel, D. Kneis, M. Kuemmerlen, S.D. Langhans,?J. Martinez-Lopez and P. Vermeiren. 2019. How to make?ecological models useful for environmental management.?Ecological Modelling 411: 108784.
Sheng, H., H. Liu, C. Wang, H. Guo, Y. Liu and Y. Yang. 2012.?Analysis of cyanobacteria bloom in the Waihai part of Dianchi lake, China. Ecological Informatics 10: 37-48.
Shin, J.K. and Y. Park. 2018. Spatiotemporal and longitudinal?variability of hydro-meteorology, basic water quality and dominant algal assemblages in the eight weir pools of?regulated river (Nakdong). Korean Journal of Ecology and?Environment 51: 268-286.
Shin, J.K., B.G. Kang and S.J. Hwang. 2016. Water-blooms?(green-tide) dynamics of algae alert system and rainfall-hydrological effects in Daecheong Reservoir, Korea. Korean?Journal of Ecology and Environment 49: 153-175.
Shin, J.K., Y. Park, N.Y. Kim and S.J. Hwang. 2022. Downstream?transport of geosmin based on harmful cyanobacterial outbreak upstream in a reservoir cascade. International Journal?of Environmnetal Research and Public Health 19: 9294.
Sibanda, M., O. Mutanga, V.G. Chimonyo, A.D. Clulow, C. Shoko,?D. Mazvimavi, T. Dube and T. Mabhaudhi. 2021. Application of drone technologies in surface water resources monitoring and assessment: A systematic review of progress,?challenges, and opportunities in the global south. Drones?5: 84.
Summers, E.J. and J.L. Ryder. 2023. A critical review of operational strategies for the management of harmful algal?blooms (HABs) in inland reservoirs. Journal of Environmental Management 330: 117141.
Teles, L.O., E. Pereira, M. Saker and V. Vasconcelos. 2008. Virtual?experimentation on cyanobacterial bloom dynamics and?its application to a temperate reservoir (Torrao, Portugal).?Lakes and Reservoirs: Research and Management 13: 135-143.
Tromas, N., N. Fortin, L. Bedrani, Y. Terrat, P. Cardoso, D. Bird,?C.W. Greer and B.J. Shapiro. 2017. Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course. The ISME (International Society for?Microbial Ecology) Journal 11: 1746-1763.
van Eck, N.J. and L. Waltman. 2007. Bibliometric mapping of the?computational intelligence field. International Journal of?Uncertainty, Fuzziness and Knowledge-Based Systems 15:?625-645.
van Eck, N.J. and L.Waltman. 2009. VOSviewer: A Computer?Program for Bibliometric Mapping. Technical Report ERS2009-005-LIS, Erasmus University Rotterdam, Erasmus?Research Institute of Management. Rotterdam, The Netherlands. 19p. http://hdl.handle.net/1765/14841
van Eck, N.J. and L. Waltman. 2010. Software survey: VOSviewer,?a computer program for bibliometric mapping. Scientometrics 84: 523-538.
van Eck, N.J., L.R.Waltman, E.C.M. Noyons and R.K. Buter.?2010a. Automatic term identification for bibliometric mapping. Scientometrics 82: 581-596.
van Eck, N.J., L.Waltman, R. Dekker and J. van den Berg. 2010b.?A comparison of two techniques for bibliometric mapping:?Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology 61:?2405-2416.
Waaijer, C.J.F., C.A. van Bochove and N.J. van Eck. 2011. On the?map: Nature and Science editorials. Scientometrics 86: 99-112.
Waltman, L., N.J. van Eck and E.C.M. Noyons. 2010. A unified?approach to mapping and clustering of bibliometric networks. Journal of Informetrics 4: 629-635.
Wang, H., R. Zhu, J. Zhang, L.Y. Ni, H. Shen and P. Xie. 2018.?A novel and convenient method for early warning of algal?cell density by chlorophyll fluorescence parameters and its?application in a highland lake. Frontiers in Plant Science 9: 869.
Watanabe, M.F., K. Harada, W.W. Carmichael and H. Fujiki. 1996. Toxic Microcystis. CRC Press, Boca Raton, London,?U.K. 262p.
Wei, B., N. Sugiura and T. Maekawa. 2001. Use of artificial neural?network in the prediction of algal blooms. Water Research?35: 2022-2028.
Welk, A., F. Recknagel, H. Cao, W.S. Chan and A. Talib. 2008.?Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary?algorithms. Ecological Informatics 3: 46-54.
Wilkinson, G.M., S.R. Carpenter, J.J. Cole, M.L. Pace, R.D. Batt,?C.D. Buelo and J.T. Kurtzweil. 2018. Early warning signals precede cyanobacterial blooms in multiple whole-lake?experiments. Ecological Monographs 88: 188-203.
World Health Organization (WHO). 2011. Management of Cyanobacteria in Drinking-water Supplies: Information for Regulators and Water Suppliers. Technical Brief WHO/FWC/WSH/15.03. 11p.
Xiao, X., J. He, H. Huang, T.R. Miller, G. Christakos, E.S. Reichwaldt, A. Ghadouani, S. Lin, X. Xu and J. Shi. 2017. A novel?single-parameter approach for forecasting algal blooms.?Water Research 108: 222-231.
Yabunaka, K., M. Hosomi and A. Murakami. 1997. Novel application of a backpropagation artificial neural network model?formulated to predict algal bloom. Water Science and Technology 36: 89-97.
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