River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques

S. I. Abba, Sinan Jasim Hadi, Jazuli Abdullahi*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

83 Scopus citations

Abstract

In this study, multi linear regression (MLR), artificial neural network (ANN) and adaptive neuro fuzzy inference system(ANFIS) techniques were developed to predict the Dissolve oxygen concentration at down stream of Agra city, using monthly input data which are dissolve oxygen(DO), pH, biological oxygen demand(BOD) and water temperature (WT) at three different places viz, Agra upstream, middle stream and downstream. Initially, 11 input parameters for all the three locations were used except DO at the downstream, then, 7 input for middle and downstream except DO at the target location and finally the downstream location was considered in the analysis. The performance was evaluated using determination coefficient (DC) and root mean square error (RMSE), the result of DO showed that both the ANN and ANFIS can be applied in modelling DO concentration in Agra city, and also indicate that, ANN model is slightly better than ANFIS and also indicates a considerable superiority to MLR.

Original languageEnglish
Pages (from-to)75-82
Number of pages8
JournalProcedia Computer Science
Volume120
DOIs
StatePublished - 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Multilinear regression
  • adaptive neuro fuzzy inference
  • artificial neural network
  • dissolve oxygen

ASJC Scopus subject areas

  • General Computer Science

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