Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant

S. I. Abba; Gozen Elkiran; Vahid Nourani

doi:10.1007/978-3-030-35249-3_88

Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant

S. I. Abba^*, Gozen Elkiran, Vahid Nourani

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The paper proposes the application of data-driven models, including wavelet neural network (WNN) and multilayer perceptron (MLP), for multi-step ahead modeling of treated chemical oxygen demand (COD_Treated ) using neuro-sensitivity input variables selection approach. Afterward, two non-linear ensemble techniques were applied to increase the prediction performance of the single models. Daily measure data obtained from new Nicosia wastewater treatment are used in this study, the performance efficiency of the models was determined in terms of Nash–Sutcliffe efficiency (NSE) and root mean squared error (RMSE). The obtained results of single models showed that WNN increased the performance accuracy up to 7% and 8% over MLP in both calibration and verification. The results also revealed the reliability of non-linear ensemble models in multi-step ahead prediction of COD_Treated, hence, ensemble modeling could efficiently improve the performance of WNN and MLP models.

Original language	English
Title of host publication	10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019
Editors	Rafik A. Aliev, Janusz Kacprzyk, Witold Pedrycz, Mo Jamshidi, Mustafa B. Babanli, Fahreddin M. Sadikogl
Publisher	Springer
Pages	683-689
Number of pages	7
ISBN (Print)	9783030352486
DOIs	https://doi.org/10.1007/978-3-030-35249-3_88
State	Published - 2020
Externally published	Yes

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1095 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Chemical oxygen demand
Ensemble technique
Multi-layer perceptron
Wastewater
Wavelet neural network

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-35249-3_88

Cite this

Abba, S. I., Elkiran, G., & Nourani, V. (2020). Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant. In R. A. Aliev, J. Kacprzyk, W. Pedrycz, M. Jamshidi, M. B. Babanli, & F. M. Sadikogl (Eds.), 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019 (pp. 683-689). (Advances in Intelligent Systems and Computing; Vol. 1095 AISC). Springer. https://doi.org/10.1007/978-3-030-35249-3_88

Abba, S. I. ; Elkiran, Gozen ; Nourani, Vahid. / Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant. 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019. editor / Rafik A. Aliev ; Janusz Kacprzyk ; Witold Pedrycz ; Mo Jamshidi ; Mustafa B. Babanli ; Fahreddin M. Sadikogl. Springer, 2020. pp. 683-689 (Advances in Intelligent Systems and Computing).

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title = "Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant",

abstract = "The paper proposes the application of data-driven models, including wavelet neural network (WNN) and multilayer perceptron (MLP), for multi-step ahead modeling of treated chemical oxygen demand (CODTreated ) using neuro-sensitivity input variables selection approach. Afterward, two non-linear ensemble techniques were applied to increase the prediction performance of the single models. Daily measure data obtained from new Nicosia wastewater treatment are used in this study, the performance efficiency of the models was determined in terms of Nash–Sutcliffe efficiency (NSE) and root mean squared error (RMSE). The obtained results of single models showed that WNN increased the performance accuracy up to 7% and 8% over MLP in both calibration and verification. The results also revealed the reliability of non-linear ensemble models in multi-step ahead prediction of CODTreated, hence, ensemble modeling could efficiently improve the performance of WNN and MLP models.",

keywords = "Chemical oxygen demand, Ensemble technique, Multi-layer perceptron, Wastewater, Wavelet neural network",

author = "Abba, {S. I.} and Gozen Elkiran and Vahid Nourani",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.",

year = "2020",

doi = "10.1007/978-3-030-35249-3_88",

language = "English",

isbn = "9783030352486",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

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editor = "Aliev, {Rafik A.} and Janusz Kacprzyk and Witold Pedrycz and Mo Jamshidi and Babanli, {Mustafa B.} and Sadikogl, {Fahreddin M.}",

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address = "Germany",

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Abba, SI, Elkiran, G & Nourani, V 2020, Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant. in RA Aliev, J Kacprzyk, W Pedrycz, M Jamshidi, MB Babanli & FM Sadikogl (eds), 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019. Advances in Intelligent Systems and Computing, vol. 1095 AISC, Springer, pp. 683-689. https://doi.org/10.1007/978-3-030-35249-3_88

Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant. / Abba, S. I.; Elkiran, Gozen; Nourani, Vahid.
10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019. ed. / Rafik A. Aliev; Janusz Kacprzyk; Witold Pedrycz; Mo Jamshidi; Mustafa B. Babanli; Fahreddin M. Sadikogl. Springer, 2020. p. 683-689 (Advances in Intelligent Systems and Computing; Vol. 1095 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant

AU - Abba, S. I.

AU - Elkiran, Gozen

AU - Nourani, Vahid

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - The paper proposes the application of data-driven models, including wavelet neural network (WNN) and multilayer perceptron (MLP), for multi-step ahead modeling of treated chemical oxygen demand (CODTreated ) using neuro-sensitivity input variables selection approach. Afterward, two non-linear ensemble techniques were applied to increase the prediction performance of the single models. Daily measure data obtained from new Nicosia wastewater treatment are used in this study, the performance efficiency of the models was determined in terms of Nash–Sutcliffe efficiency (NSE) and root mean squared error (RMSE). The obtained results of single models showed that WNN increased the performance accuracy up to 7% and 8% over MLP in both calibration and verification. The results also revealed the reliability of non-linear ensemble models in multi-step ahead prediction of CODTreated, hence, ensemble modeling could efficiently improve the performance of WNN and MLP models.

AB - The paper proposes the application of data-driven models, including wavelet neural network (WNN) and multilayer perceptron (MLP), for multi-step ahead modeling of treated chemical oxygen demand (CODTreated ) using neuro-sensitivity input variables selection approach. Afterward, two non-linear ensemble techniques were applied to increase the prediction performance of the single models. Daily measure data obtained from new Nicosia wastewater treatment are used in this study, the performance efficiency of the models was determined in terms of Nash–Sutcliffe efficiency (NSE) and root mean squared error (RMSE). The obtained results of single models showed that WNN increased the performance accuracy up to 7% and 8% over MLP in both calibration and verification. The results also revealed the reliability of non-linear ensemble models in multi-step ahead prediction of CODTreated, hence, ensemble modeling could efficiently improve the performance of WNN and MLP models.

KW - Chemical oxygen demand

KW - Ensemble technique

KW - Multi-layer perceptron

KW - Wastewater

KW - Wavelet neural network

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U2 - 10.1007/978-3-030-35249-3_88

DO - 10.1007/978-3-030-35249-3_88

M3 - Conference contribution

AN - SCOPUS:85089224973

SN - 9783030352486

T3 - Advances in Intelligent Systems and Computing

SP - 683

EP - 689

BT - 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019

A2 - Aliev, Rafik A.

A2 - Kacprzyk, Janusz

A2 - Pedrycz, Witold

A2 - Jamshidi, Mo

A2 - Babanli, Mustafa B.

A2 - Sadikogl, Fahreddin M.

PB - Springer

ER -

Abba SI, Elkiran G, Nourani V. Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant. In Aliev RA, Kacprzyk J, Pedrycz W, Jamshidi M, Babanli MB, Sadikogl FM, editors, 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019. Springer. 2020. p. 683-689. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-35249-3_88

Non-linear ensemble modeling for multi-step ahead prediction of treated cod in wastewater treatment plant

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this