Open Access
Issue
E3S Web Conf.
Volume 17, 2017
9th Conference on Interdisciplinary Problems in Environmental Protection and Engineering EKO-DOK 2017
Article Number 00089
Number of page(s) 8
DOI https://doi.org/10.1051/e3sconf/20171700089
Published online 24 May 2017
  1. J. Łomotowski, A. Szpindor, Nowoczesne systemy oczyszczania ścieków (2002) [Google Scholar]
  2. A.M.P. Martins, K.R. Pagilla, J.J. Heijnen, M.C.M. Van Loosdrecht, Bulking filamentous sludge - a critical review, Wat. Res. 38, 793 (2004) [CrossRef] [Google Scholar]
  3. Z. Dymaczewski, J.A. Oleszkiewicz, M. M. Sozański, Poradnik eksploatatora oczyszczalni ścieków. Wydanie II (1997) [Google Scholar]
  4. I. Lou, Y. Zhao, Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network, Math Probl Eng 2012, 1 (2012) [CrossRef] [Google Scholar]
  5. B. Szeląg, J. Gawdzik, Application of Selected Methods of Artificial Intelligence to Activated Sludge Settleability Predictions, Pol J Environ Stud 25, 1709 (2016) [Google Scholar]
  6. F. Li, J. Qiao J., Y. Han, C. Yang, A self - organizing cascade neural network with random weights for nonlinear system modeling, Appl Soft Comput 42, 184 (2016) [CrossRef] [Google Scholar]
  7. J. Bayo, J.M. Angosto, J. M, J. Serrano-Aniorte, Evaluation of physicochemical parameters influencing bulking episodes in a municipal wastewater treatment plant. Water Pollution VIII: Modelling, Monitoring and Management, 531 (2006) [CrossRef] [Google Scholar]
  8. E. Bezak-Mazur, R. Stoińska, B. Szeląg, Ocena wpływu parametrów operacyjnych i występowania bakterii nitkowatych na objętościowy indeks osadu czynnego – studium przypadku, Rocznik Ochrona Środowiska 18, 487 (2016) [Google Scholar]
  9. L. Belanche, J. Valdes, J. Comas, I. Roda, M. Poch, Prediction of the bulking phenomenon in wastewater treatment plants, Artificial Intelligence in Engineering 14, 307 (2000) [CrossRef] [Google Scholar]
  10. J. Łomotowski, M. Dańczuk, Application of the microwave energy to the hygienization of sewage sludge, EPE J 36, 77 (2010) [Google Scholar]
  11. T. Heyer, J. Stamm, Levee reliability analysis using logistic regression models – abilities, limitations and practical considerations, Georisk Assessment and Management of Risk for Engineered Systems and Geohazards 7, 77 (2013) [CrossRef] [Google Scholar]
  12. D.H. Tran, A.W.M. Ng, B.J.C. Perera, S. Burn, P. Davis, Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes, Urban Water J 3, 175 (2006) [CrossRef] [Google Scholar]
  13. A.M. Ramos-Cañón, L.F. Prada-Sarmiento, M.G. Trujillo-Vela, J.P. Macías, A.C. Santos-R, Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia, Landslides 13, 671 (2016) [CrossRef] [Google Scholar]
  14. E. Gatnar, Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji (2012) [Google Scholar]
  15. F. Harrell, Regression Modeling Strategies with Application to Linear Models, Logistic Regression, and Survival Analysis (2001) [Google Scholar]

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