References
- J. Hakanen, K. Miettinen, K. Sahlstedt, Wastewater treatment:
new insight provided by interactive multiobjective optimization,
Decis. Support Syst., 51 (2010) 328–337.
- H. Han, L. Zhang, Y. Hou, J. Qiao, Nonlinear model predictive
control based on a self-organizing recurrent neural network,
IEEE Trans. Neur. Networks Learn., 27 (2016) 402–415.
- A. Saptoro, State of the art in the development of adaptive
soft sensors based on just-in-time models, Procedia Chem.,
9 (2014) 226–234.
- B. Szelag, K. Barbusinski, J. Studzinski, L. Bartkiewicz,
Prediction of wastewater quality indicators at the inflow to
the wastewater treatment plant using data mining methods,
E3S Web Conf., (2017) 1–8.
- P. Yongeun, L. Mayzonee, M.K. Young, Development of
enhanced groundwater arsenic prediction model using machine
learning approaches in Southeast Asian countries, Desal. Water
Treat., 57 (2016) 12227–12236.
- K. Ding, J. Zhang, X. Zhu, The model of pump head data
mining based on SVM, Appl. Mech. Mater., 2685 (2013) 3263–3268.
- A. Sharafati, S. Asadollah, M. Hosseinzadeh, The potential of
new ensemble machine learning models for effluent quality
parameters prediction and related uncertainty, Process Saf.
Environ., 140 (2020) 68–78.
- V. Sousa, J. Matos, N. Matias, I. Meireles, Statistical comparison
of the performance of data-based models for sewer condition
modeling, Struct. Infrastruct. Eng., 15 (2019) 1680–1693.
- S. Włodzimierz, K. Wojciech, Determination of the optimal
operational parameters for a three-phase fluidised bed
bioreactor with a light biomass support when used in treatment
of phenolic wastewaters, Biochem. Eng. J., 20 (2014) 49–56.
- C. Song, H. Wang, P. Li, A Receding Optimization Control
Policy for Production Systems with Quadratic Inventory Costs,
IFAC Proceedings Volumes, 2004, pp. 713–717.
- W. Ang, W.M Abdul, H. Nidal, P.L Choe, A review on the
applicability of integrated/hybrid membrane processes in water
treatment and desalination plants, Desalination, 363 (2015)
2–18.
- S. Meng, Y.R. Shen, E. Wang, Basic science of water: challenges
and current status towards a molecular picture, Nano Res.,
8 (2015) 3085–3110.
- E.S. Rigobello, A.D. Dantas, L.D. Bernardo, Removal of
diclofenac by conventional drinking water treatment processes
and granular activated carbon filtration, Chemosphere,
92 (2013) 184–191.
- M. Vliet, J. Yearsley, W. Franssen, F. Ludwig, Coupled daily
streamflow and water temperature modelling in large river
basins, Hydrol. Earth Syst. Sci., 16 (2012) 4303–4321.
- L. Zhang, C. Chen, J. Bu, D. Cai, X. He, Active Learning based
on locally linear reconstruction, IEEE Trans. Pattern Anal.,
33 (2011) 2026–2038.
- C. Chen, L. Zhang, J. Bu, Constrained Laplacian Eigenmap for
dimensionality reduction, Neurocomputing, 73 (2010) 951–958.
- T.K. Ho, Random Decision Forest, Proceedings of the
3rd International Conference on Document Analysis and
Recognition, 1995, pp. 278–282.
- X. Wang, L. Wang, N. Li, An application of decision tree based
on ID3, Phys. Procedia, 25 (2012) 1017–1021
- V.N. Vapnik, An overview of statistical learning theory,
IEEE Trans. Neural Network, 10 (1999) 988–999.
- G. Hong, J. Kwan, J. Lim, J. Jo, Prediction of effluent
concentration in a wastewater treatment plant using machine
learning models, J. Environ. Sci., 32 (2015) 90–101.
- H. Yoon, S.C. Jun, Y. Hyun, A comparative study of artificial
neural networks and support vector machines for predicting
groundwater levels in a coastal aquifer, J. Hydrol., 396 (2011)
128–138.
- Q.J. Wang, Using genetic algorithms to optimise model
parameters, Environ. Model. Softw., 12 (1997) 27–34.