References

  1. F. Hernández-del-Olmo, E. Gaudioso, R. Dormido, N. Duro, Energy and environmental efficiency for the n-ammonia removal process in wastewater treatment plants by means of reinforcement learning, Energies, 9 (2016) 755, doi: 10.3390/ en9090755.
  2. Z.J. Zhang, A. Kusiak, Y.H. Zeng, X.P. Wei, Modeling and optimization of a wastewater pumping system with datamining methods, Appl. Energy, 164 (2016) 303–311.
  3. D. Torregrossa, J. Hansen, F. Hernández-Sancho, A. Cornelissen, G. Schutz, U. Leopold, A data-driven methodology to support pump performance analysis and energy efficiency optimization in wastewater treatment plants, Appl. Energy, 208 (2017) 1430–1440.
  4. J.-J. Zhu, L. Kang, P.R. Anderson, Predicting influent biochemical oxygen demand: balancing energy demand and risk management, Water Res., 128 (2018) 304–313.
  5. T.Y. Pai, P.Y. Yang, S.C. Wang, M.H. Lo, C.F. Chiang, J.L. Kuo, H.H. Chu, H.C. Su, L.F. Yu, H.C. Hu, Y.H. Chang, Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality, Appl. Math. Modell., 35 (2011) 3674–3684.
  6. H. Guo, K. Jeong, J. Lim, J. Jo, Y.M. Kim, J. Pyo Park, J.H. Kim, K.H. Cho, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, J. Environ. Sci. (China), 32 (2015) 90–101.
  7. X. Liu, Y. Chen, H. Ge, P. Fazio, G. Chen, Numerical investigation for thermal performance of exterior walls of residential buildings with moisture transfer in hot summer and cold winter zone of China, Energy Build., 93 (2015) 259–268.
  8. K. Gibert, J. Izquierdo, M. Sànchez-Marrè, S.H. Hamilton, I. Rodríguez-Roda, G. Holmes, Which method to use? an assessment of data mining methods in Environmental Data Science, Environ. Modell. Software, 110 (2018) 3–27.
  9. F. Harrou, A. Dairi, Y. Sun, M. Senouci, Statistical monitoring of a wastewater treatment plant: a case study, J. Environ. Manage., 223 (2018) 807–814.
  10. B. Khalil, J. Adamowski, A. Abdin, A. Elsaadi, A statistical approach for the estimation of water quality characteristics of ungauged streams/watersheds under stationary conditions, J. Hydrol., 569 (2019) 106–116.
  11. J.J. Lee, C.S. Jang, C.W. Liu, C.P. Liang, S.W. Wang, Determining the probability of arsenic in groundwater using a parsimonious model, Environ. Sci. Technol., 43 (2009) 6662–6668.
  12. X. Wang, H. Ratnaweera, J.A. Holm, V. Olsbu, Statistical monitoring and dynamic simulation of a wastewater treatment plant: a combined approach to achieve model predictive control, J. Environ. Manage., 193 (2017) 1–7.
  13. A. Kusiak, X. Wei, Prediction of methane production in wastewater treatment facility: a data-mining approach, Ann. Oper. Res., 216 (2014) 71–81.
  14. A. Asadi, A. Verma, K. Yang, B. Mejabi, Wastewater treatment aeration process optimization: a data mining approach, J. Environ. Manage., 203 (2017) 630–639.
  15. R. Avila, B. Horn, E. Moriarty, R. Hodson, E. Moltchanova, Evaluating statistical model performance in water quality prediction, J. Environ. Manage., 206 (2018) 910–919.
  16. S. Yamijala, S.D. Guikema, K. Brumbelow, Statistical models for the analysis of water distribution system pipe break data, Reliab. Eng. Syst. Saf., 94 (2009) 282–293.
  17. Y. Kleiner, B. Rajani, Comparison of four models to rank failure likelihood of individual pipes, J. Hydroinf., 14 (2012) 659–681.
  18. A. Robles-Velasco, P. Cortés, J. Muñuzuri, L. Onieva, Prediction of pipe failures in water supply networks using logistic regression and support vector classification, Reliab. Eng. Syst. Saf., 196 (2020) 106754, doi: 10.1016/j.ress.2019.106754.
  19. W. Thoe, M. Gold, A. Griesbach, M. Grimmer, M.L. Taggart, A.B. Boehm, Predicting water quality at Santa Monica Beach: evaluation of five different models for public notification of unsafe swimming conditions, Water Res., 67 (2014) 105–117.
  20. T.K. Saha, S. Pal, Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches, Ecol. Indic., 98 (2019) 251–265.
  21. N.K.C. Twarakavi, J.J. Kaluarachchi, Aquifer vulnerability assessment to heavy metals using ordinal logistic regression, Ground Water, 43 (2005) 200–214.
  22. E. Dogan, A. Ates, E.C. Yilmaz, B. Eren, Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand, Environ. Prog., 27 (2008) 439–446.
  23. B. Szeląg, K. Barbusiński, J. Studziński, Application of the model of sludge volume index forecasting to assess reliability and improvement of wastewater treatment plant operating conditions, Desal. Water Treat., 140 (2019) 143–154.
  24. A. Verma, X. Wei, A. Kusiak, Predicting the total suspended solids in wastewater: a data-mining approach, Eng. Appl. Artif. Intell., 26 (2013) 1366–1372.
  25. A. Mair, A.I. El-Kadi, Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA, J. Contam. Hydrol., 153 (2013) 1–23.
  26. M.C. Maniquiz, S. Lee, L.H. Kim, Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables, J. Environ. Sci., 22 (2010) 946–952.
  27. S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review, J. Biomed. Inf., 35 (2002) 352–359.
  28. W. Yoo, B.A. Ference, M.L. Cote, A. Schwartz, A comparison of logistic regression, logic regression, classification tree, and random forests to identify effective gene-gene and geneenvironmental interactions, Int. J. Appl. Sci. Technol., 2 (2012) 268.
  29. Y. Lee, J.A. Nelder, Y. Pawitan, Generalized Linear Models with Random Effects, Chapman and Hall/CRC, 2018.
  30. A. Witteveen, G.F. Nane, I.M.H. Vliegen, S. Siesling, M.J. IJzerman, Comparison of logistic regression and bayesian networks for risk prediction of breast cancer recurrence, Med. Decis. Mak., 38 (2018) 822–833.
  31. B. Peeters, R. Dewil, I.Y. Smets, Improved process control of an industrial sludge centrifuge-dryer installation through binary logistic regression modeling of the fouling issues, J. Process Control, 22 (2012) 1387–1396.
  32. N. Deepnarain, S. Kumari, J. Ramjith, F.M. Swalaha, V. Tandoi, K. Pillay, F. Bux, A logistic model for the remediation of filamentous bulking in a biological nutrient removal wastewater treatment plant, Water Sci. Technol., 72 (2015) 391–405.
  33. B. Szeląg, K. Barbusiński, J. Studziński, Activated sludge process modelling using selected machine learning techniques, Desal. Water Treat., 117 (2018) 78–87.
  34. B. Szelag, R. Suligowski, J. Studziński, F. De Paola, Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach, Hydrol. Earth Syst. Sci., 24 (2020) 595–614.
  35. Y.P. Lin, B.Y. Cheng, H.J. Chu, T.K. Chang, H.L. Yu, Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods, Geoderma, 163 (2011) 275–282.
  36. B. Petersen, K. Gernaey, M. Henze, P.A. Vanrolleghem, Evaluation of an ASM1 model calibration procedure on a municipal-industrial wastewater treatment plant, J. Hydroinf., 4 (2002) 15–38.
  37. F.E. Harrell, Regression Modeling Strategies, Springer International Publishing, Cham, 2015.
  38. B. Szeląg, J. Drewnowski, G. Łagód, D. Majerek, E. Dacewicz, F. Fatone, Soft sensor application in identification of the activated sludge bulking considering the technological and economical aspects of smart systems functioning, Sensors (Switzerland), 20 (2020) 1941, doi: 10.3390/s20071941.
  39. L. Zou, H. Li, S. Wang, K. Zheng, Y. Wang, G. Du, J. Li, Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin, Front. Environ. Sci. Eng., 13 (2019) 83.
  40. M. Kim, Y. Kim, H. Kim, W. Piao, C. Kim, Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant, Front. Environ. Sci. Eng., 10 (2016) 299–310.
  41. M. Ebrahimi, E.L. Gerber, T.D. Rockaway, Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis, J. Environ. Manage., 193 (2017) 234–246.
  42. G. Langergraber, J. Alex, N. Weissenbacher, D. Woerner, M. Ahnert, T. Frehmann, N. Halft, L. Hobus, M. Plattes, V. Spering, S. Winkler, Generation of diurnal variation for influent data for dynamic simulation, Water Sci. Technol., 57 (2008) 1483–1486.
  43. X. Wang, K. Kvaal, H. Ratnaweera, Characterization of influent wastewater with periodic variation and snow melting effect in cold climate area, Comput. Chem. Eng., 106 (2017) 202–211.
  44. M. Ansari, F. Othman, A. El-Shafie, Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant, Sci. Total Environ., 722 (2020) 137878, doi: 10.1016/j.scitotenv.2020.137878.
  45. M. Ahnert, C. Marx, P. Krebs, V. Kuehn, A black-box model for generation of site-specific WWTP influent quality data based on plant routine data, Water Sci. Technol., 74 (2016) 2978–2986.
  46. D. Rousseau, F. Verdonck, O. Moerman, R. Carrette, C. Thoeye, J. Meirlaen, P.A. Vanrolleghem, Development of a risk assessment based technique for design/retrofitting of WWTPs, Water Sci. Technol., 43 (2001) 287–294.
  47. Y. Sun, Z. Chen, G. Wu, Q. Wu, F. Zhang, Z. Niu, H.Y. Hu, Characteristics of water quality of municipal wastewater treatment plants in China: Implications for resources utilization and management, J. Cleaner Prod., 131 (2016) 1–9.