References

  1. H.W. Paerl, V.J. Paul, Climate change: links to global expansion of harmful cyanobacteria, Water Res., 46 (2012) 1349–1363.
  2. G.A. Codd, Cyanobacterial toxins, the perception of water quality, and the prioritisation of eutrophication control, Ecol. Eng., 16 (2000) 51–60.
  3. F. Recknagel, Applications of machine learning to ecological modelling, Ecol. Modell., 146 (2001) 303–310.
  4. N. Muttil, K. Chau, Neural network and genetic programming for modelling coastal algal blooms, Int. J. Environ. Pollut., 28 (2006) 223–238.
  5. N. Jung, I. Popescu, P. Kelderman, D.P. Solomatine, R.K. Price, Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea, J. Hydroinf., 12 (2010) 262–274.
  6. G.G. Moisen, Classification and Regression Trees, Encyclopedia of Ecology, Volume 1, Elsevier, Oxford, U.K, 2008, pp. 582–588.
  7. G. De’ath, K.E. Fabricius, Classification and regression trees: a powerful yet simple technique for ecological data analysis, Ecology, 81 (2000) 3178–3192.
  8. A. Peretyatko, S. Teissier, S. De Backer, L. Triest, Classification trees as a tool for predicting cyanobacterial blooms, Hydrobiologia, 689 (2012) 131–146.
  9. M. Rodrigues, J. de la Riva, An insight into machine-learning algorithms to model human-caused wildfire occurrence, Environ. Modell. Software, 57 (2014) 192–201.
  10. H. He, E.A. Garcia, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng., 21 (2009) 1263–1284.
  11. S. Ertekin, J. Huang, L. Bottou, L. Giles, Learning on the Border: Active Learning in Imbalanced Data Classification, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, ACM, Lisbon, Portugal, 2007, pp. 127–136.
  12. A. Estabrooks, T. Jo, N. Japkowicz, A multiple resampling method for learning from imbalanced data sets, Comput. Intell., 20 (2004) 18–36.
  13. B. Gong, J. Ordieres-Meré, Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: case study of Hong Kong, Environ. Modell. Software, 84 (2016) 290–303.
  14. M.Y. Suh, B.H. Kim, K.S. Bae, Fluctuation of environmental factors and dynamics of phytoplankton communities in lower part of the Han River, Korean J. Ecol. Environ., 40 (2007) 395–402.
  15. T.K. Kim, J.H. Choi, K.J. Lee, Y.B. Kim, S.J. Yu, Study on introduction to predicting indicator of cyanobacteria dominance in algae bloom warning system of Hangang Basin, J. Korean Soc. Environ. Eng., 36 (2014) 378–385.
  16. X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, S.Y. Philip, Top 10 algorithms in data mining, Knowl. Inf. Syst., 14 (2008) 1–37.
  17. C.D. Sutton, Classification and Regression Trees, Bagging, and Boosting, Handbook of Statistics, Elsevier, Vol. 24, 2005, pp. 303–329.
  18. L. Rokach, Ensemble-based classifiers, Artif. Intell. Rev., 33 (2010) 1–39.
  19. L. Breiman, Bagging Predictors, Machine Learning, Vol. 24, 1996, pp. 123–140.
  20. L. Breiman, Random Forests, Machine Learning, Vol. 45, 2001, pp. 5–32.
  21. A. Liaw, M. Wiener, Classification and Regression by randomForest, Vol. 2, 2002, pp. 18–22.
  22. N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16 (2002) 321–357.
  23. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2016, Available at: https://www.R-project.org/.
  24. M. Greiner, D. Pfeiffer, R. Smith, Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests, Prev. Vet. Med., 45 (2000) 23–41.
  25. T.G. Dietterich, Ensemble Learning, The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, England, 2nd ed., 2002, pp. 405–408.
  26. Y. Zhang, M. Bocquet, V. Mallet, C. Seigneur, A. Baklanov, Real-time air quality forecasting, part I: history, techniques, and current status, Atmos. Environ., 60 (2012) 632–655.
  27. A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, D. Khalili, The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes, Med. Decis. Making, 36 (2016) 137–144.
  28. B.W. Ibelings, M. Vonk, H.F. Los, D.T. van der Molen, W.M. Mooij, Fuzzy modeling of cyanobacterial surface waterblooms: validation with NOAA‐AVHRR satellite images, Ecol. Appl., 13 (2003) 1456–1472.
  29. K.D. Joehnk, J. Huisman, J. Sharples, B. Sommeijer, P.M. Visser, J.M. Stroom, Summer heatwaves promote blooms of harmful cyanobacteria, Global Change Biol., 14 (2008) 495–512.
  30. W.M. Mooij, S. Hülsmann, L.N. De Senerpont Domis, B.A. Nolet, P.L. Bodelier, P.C. Boers, L.M.D. Pires, H.J. Gons, B.W. Ibelings, R. Noordhuis, The impact of climate change on lakes in the Netherlands: a review, Aquat. Ecol., 39 (2005) 381–400.
  31. H.W. Paerl, J. Huisman, Blooms like it hot, Science, 320 (2008) 57–58.
  32. B.J. Robson, D.P. Hamilton, Summer flow event induces a cyanobacterial bloom in a seasonal Western Australian estuary, Mar. Freshwater Res., 54 (2003) 139–151.
  33. J. Elliott, I. Jones, S. Thackeray, Testing the sensitivity of phytoplankton communities to changes in water temperature and nutrient load, in a temperate lake, Hydrobiologia, 559 (2006) 401–411.
  34. K. Ha, M. Jang, G. Joo, Spatial and temporal dynamics of phytoplankton communities along a regulated river system, the Nakdong River, Korea, Hydrobiologia, 470 (2002) 235–245.
  35. K. Jeong, D. Kim, G. Joo, Delayed influence of dam storage and discharge on the determination of seasonal proliferations of Microcystis aeruginosa and Stephanodiscus hantzschii in a regulated river system of the lower Nakdong River (South Korea), Water Res., 41 (2007) 1269–1279.
  36. Water Environment Ecology Team, Han River (Water Recreational Activity Area) Algae Warning System Operation Result of 2016, Water Environment Research Department, Seoul Metropolitan Government, 2016.