References
- W.K. Dodds, W.W. Bouska, J.L. Eitzmann, T.J. Pilger, K.L. Pitts,
A.J. Riley, J.T. Schloesser, D.J. Thornbrugh, Eutrophication
of U.S. freshwaters: analysis of potential economic damages,
Environ. Sci. Technol., 43 (2009) 12–19.
- M.B. Edlund, D.R. Engstrom, L.D. Triplett, B.M. Lafrancois,
P.R. Leavitt, Twentieth century eutrophication of the St. Croix
River (Minnesota–Wisconsin, USA) reconstructed from the
sediments of its natural impoundment, J. Paleolimnol., 41 (2009)
641–657.
- H.-I. Eum, S.P. Simonovic, Integrated reservoir management
system for adaptation to climate change: the Nakdong River
Basin in Korea, Water Resour. Manage., 24 (2010) 3397–3417.
- G.J. Smith, V. Daniels, Algal blooms of the 18th and 19th
centuries, Toxicon, 142 (2018) 42–44.
- A.F. Bouwman, L.J.M. Boumans, N.H. Batjes, Estimation of
global NH3 volatilization loss from synthetic fertilizers and
animal manure applied to arable lands and grasslands, Global
Biogeochem. Cycles, 16 (2002) 8-1–8-14.
- P.M. Glibert, J.M. Burkholder, The Complex Relationships
Between Increases in Fertilization of the Earth, Coastal
Eutrophication
and Proliferation of Harmful Algal Blooms,
Ecology of Harmful Algae, Springer, Berlin, Heidelberg, 2006,
pp. 341–354.
- S. Ding, M. Chen, M. Gong, X. Fan, B. Qin, H. Xu, S. Gao, Z. Jin,
D.C.W. Tsang, C. Zhang, Internal phosphorus loading from
sediments causes seasonal nitrogen limitation for harmful algal
blooms, Sci. Total Environ., 625 (2018) 872–884.
- K.J. Flynn, A. Mitra, Building the “perfect beast”: modelling
mixotrophic plankton, J. Plankton Res., 31 (2009) 965–992.
- P.M. Glibert, J.I. Allen, A.F. Bouwman, C.W. Brown, K.J. Flynn,
A.J. Lewitus, C.J. Madden, Modeling of HABs and eutrophication:
status, advances, challenges, J. Mar. Syst., 83 (2010)
262–275.
- S. Bae, D. Seo, Analysis and modeling of algal blooms in the
Nakdong River, Korea, Ecol. Modell., 372 (2018) 53–63.
- Eutrophication of Waters: Monitoring, Assessment and Control,
Organisation for Economic Co-operation and Development,
OECD Publications and Information Center, Washington, 1982.
- M.V. Hoyer, J.R. Jones, Factors affecting the relation between
phosphorus and chlorophyll a in Midwestern reservoirs, Can. J.
Fish. Aquat. Sci., 40 (1983) 192–199.
- V.H. Smith, G.D. Tilman, J.C. Nekola, Eutrophication: impacts
of excess nutrient inputs on freshwater, marine, and terrestrial
ecosystems, Environ. Pollut., 100 (1999) 179–196.
- W.R. Hill, S.E. Fanta, B.J. Roberts, Quantifying phosphorus
and light effects in stream algae, Limnol. Oceanogr., 54 (2009)
368–380.
- D.E. Canfield Jr., Prediction of chlorophyll a concentrations
in Florida lakes: the importance of phosphorus and nitrogen,
J. Am. Water Resour. Assoc., 19 (1983) 255–262.
- V.L.M. Huszar, N.F. Caraco, F. Roland, J. Cole, Nutrient–chlorophyll relationships in tropical–subtropical lakes: do
temperate models fit?, Biogeochemistry, 79 (2006) 239–250.
- G. Phillips, O.-P. Pietiläinen, L. Carvalho, A. Solimini, A. Lychees
Solheim, A.C. Cardoso, Chlorophyll–nutrient relationships
of different lake types using a large European dataset, Aquat.
Ecol., 42 (2008) 213–226.
- Y.T. Prairie, C.M. Duarte, J. Kalff, Unifying nutrient–chlorophyll
relationships in lakes, Can. J. Fish. Aquat. Sci., 46 (1989)
1176–1182.
- G. Borics, L. Nagy, S. Miron, I. Grigorszky, Z. László-Nagy, B.A. Lukács, L. G-Tóth, G. Várbíró, Which factors
affect phytoplankton biomass in shallow eutrophic lakes?,
Hydrobiologia, 714 (2013) 93–104.
- K.-S. Jeong, G.-J. Joo, H.-W. Kim, K. Ha, F. Recknagel, Prediction
and elucidation of phytoplankton dynamics in the Nakdong
River (Korea) by means of a recurrent artificial neural network,
Ecol. Modell., 146 (2001) 115–129.
- D.F. Millie, G.R. Weckman, W.A. Young II, J.E. Ivey, H.J. Carrick,
G.L. Fahnenstiel, Modeling microalgal abundance with artificial
neural networks: demonstration of a heuristic ‘Grey-Box’ to
deconvolve and quantify environmental influencesk, Environ.
Model. Software, 38 (2012) 27–39.
- A.N. Blauw, P. Anderson, M. Estrada, M. Johansen, J. Laanemets,
L. Paperzak, D. Purdie, R. Raine, E. Vahtera, The use of fuzzy
logic for data analysis and modelling of European harmful algal
blooms: results of the HABES project, Afr. J. Mar. Sci., 28 (2006)
365–369.
- B. Paudel, D. Velinsky, T. Belton, H. Pang, Spatial variability
of estuarine environmental drivers and response by
phytoplankton: a multivariate modeling approach, Ecol. Inf.,
34 (2016) 1–12.
- S. Chen, S.A. Billings, Neural networks for nonlinear dynamic
system modelling and identification, Int. J. Control, 56 (1992)
319–346.
- G. Gal, M. Skerjanec, N. Atanasova, Fluctuations in water level
and the dynamics of zooplankton: a data-driven modelling
approach, Freshwater Biol., 58 (2013) 800–816.
- M. Kuhn, K. Johnson, Applied Predictive Modeling, Vol. 26,
Springer, New York, 2013.
- D.P. Solomatine, K.N. Dulal, Model trees as an alternative to
neural networks in rainfall-runoff modeling, Hydrol. Sci. J.,
48 (2003) 399–411.
- B. Bhattacharya, D.P. Solomatine, Neural networks and M5
model trees in modelling water level–discharge relationship,
Neurocomputing, 63 (2005) 381–396.
- S. Schnier, X. Cai, Prediction of regional streamflow frequency
using model tree ensembles, J. Hydrol., 517 (2014) 298–309.
- S. Heddam, O. Kisi, Modelling daily dissolved oxygen
concentration using least square support vector machine,
multivariate adaptive regression splines and M5 model tree,
J. Hydrol., 229 (2018) 499–509.
- S.J. Moe, S. Haande, R.-M. Couture, Climate change,
cyanobacteria blooms and ecological status of lakes: a Bayesian
network approach, Ecol. Modell., 337 (2016) 330–347.
- C.M. Mutshinda, Z.V. Finkel, A.J. Irwin, Which environmental
factors control phytoplankton populations? A Bayesian variable
selection approach, Ecol. Modell., 269 (2013) 1–8.
- Y. Park, Y.A. Pachepsky, K.H. Cho, D.J. Jeon, J.H. Kim, Stressor–response modeling using the 2D water quality model and
regression trees to predict chlorophyll-a in a reservoir system,
J. Hydrol., 529 (2015) 805–815.
- S. Han, E. Kim, S. Kim, The water quality management in
the Nakdong River watershed using multivariate statistical
techniques, KSCE J. Civil Eng., 13 (2009) 97–105.
- H.-W. Kim, S.-J. Hwang, K.-H. Chang, M.-H. Jang, G.-J. Joo,
N. Walz, Longitudinal difference in Zooplankton grazing on
phyto- and bacterioplankton in the Nakdong River (Korea),
Int. Rev. Hydrobiol., 87 (2002) 281–293.
- Z. Yang, P. Xu, D. Liu, J. Ma, D. Ji, Y. Cui, Hydrodynamic
mechanisms underlying periodic algal blooms in the tributary
bay of a subtropical reservoir, Ecol. Eng., 120 (2018) 6–13.
- M.J.A. Berry, G.S. Linoff, Data Mining Techniques: For
Marketing, Sales, and Customer Support, Wiley, USA, 1997.
- T.S. Lim, W.Y. Loh, Y.S. Shin, An Empirical Comparison of
Decision Trees and Other Classification Methods, Technical
Report 979, Department of Statistics, UW Madison, 1997.
- G.V. Kass, An exploratory technique for investigating large
quantities of categorical data, Appl. Stat., 29 (1980) 119–127.
- J. Magidson, The use of the new ordinal algorithm in
CHAID to target profitable segments, J. Database Mark.,
1 (1993) 29–48.
- W.Y. Loh, Fifty years of classification and regression trees, Int.
Stat. Rev., 82 (2014) 329–348.
- S. Kim, S. Kim, Spatial water quality analysis of main stream of
Nakdong River considering the inflow of tributaries, J. Korean
Soc. Water Environ., 33 (2017) 640–649.
- J. Kӧhler, Origin and succession of phytoplankton in a riverlake
system (Spree, Germany), Hydrobiologia, 289 (1994) 73–83.
- D.J. McQueen, D.R.S. Lean, Influence of water temperature and
nitrogen to phosphorus ratios on the dominance of blue-green
algae in Lake St. George, Ontario, Can. J. Fish. Aquat. Syst.,
44 (1987) 598–604.
- R.E. Hecky, P. Kilham, Nutrient limitation of phytoplankton
in freshwater and marine environments: a review of recent
evidence on the effects of enrichment, Limnol. Oceanogr.,
33 (1988) 796–822.
- D.J. Conley, H.W. Paerl, R.W. Howarth, D.F. Boesch,
S.P. Seitzinger, K.E. Havens, C. Lancelot, G.E. Likens, Policy
Forum Ecology/Controlling eutrophication: nitrogen and
phosphorus, Science, 323 (2009) 1014–1015.