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 proceedingConference contributionpeer-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 (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.

Original languageEnglish
Title of host publication10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019
EditorsRafik A. Aliev, Janusz Kacprzyk, Witold Pedrycz, Mo Jamshidi, Mustafa B. Babanli, Fahreddin M. Sadikogl
PublisherSpringer
Pages683-689
Number of pages7
ISBN (Print)9783030352486
DOIs
StatePublished - 2020
Externally publishedYes

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1095 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

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