Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant

Providing a robust and reliable model is essential for hydro-environmental and public health engineering perspectives, including water treatment plants (WTPs). The current research develops an emerging evolutionary data-intelligence model: extreme learning machine (ELM) integrated with kernel princi...

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Veröffentlicht in:Journal of water process engineering Jg. 33; S. 101081
Hauptverfasser: Abba, S.I., Pham, Quoc Bao, Usman, A.G., Linh, Nguyen Thi Thuy, Aliyu, D.S., Nguyen, Quyen, Bach, Quang-Vu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.02.2020
Schlagworte:
Cross-validation
Data-driven algorithms
Extreme learning marchine
Kano-Tamburawa
Principal component analysis
Water treatment plant
Extreme learning marchine
Water treatment plant
Data-driven algorithms
Kano-Tamburawa
Cross-validation
Principal component analysis
ISSN:2214-7144, 2214-7144
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Zusammenfassung:Providing a robust and reliable model is essential for hydro-environmental and public health engineering perspectives, including water treatment plants (WTPs). The current research develops an emerging evolutionary data-intelligence model: extreme learning machine (ELM) integrated with kernel principal component analysis (KPCA) to predict the performance of the Tamburawa WTP in Kano, Nigeria. A traditional feed-forward neural network (FFNN) and a classical linear autoregressive (AR) models were also employed to compare the predictive performance. For this purpose, different input data with the corresponding treated pH, turbidity, total dissolve solids, and hardness as the target variables obtained from the WTP were used. The predictive models are evaluated based on the three numerical indices, namely Nash-Sutcliffe (NC), root mean squared error (RMSE) and mean absolute percentage error (MAPE). To examine the similarities and differences between the observed and predicted values, a two-dimension graphical diagram (i.e., Taylor diagram) was also utilized. The predictive results revealed the potential of KPCA-ELM, which exhibited a high level of accuracy in comparison to the single models for all the considered variables with a slight exception in terms of pH prediction. Two different model combination were built for each single (FFNN, ELM, and AR) model and KPCA algorithms (KPCA-FFNN, KPCA-ELM, and KPCA-AR). The results also depicted that both ELM and FFNN models demonstrated prediction skill and therefore, can serve as reliable models. The outcomes may contribute to the aforementioned modeling of the treated parameters and provides a reference benchmark for wastewater management and control in the Tamburawa WTP.
ISSN:2214-7144
2214-7144
DOI:10.1016/j.jwpe.2019.101081