Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches

S. I. Abba, M. S. Gaya*, M. L. Yakubu, M. U. Zango, R. A. Abdulkadir, M. A. Saleh, A. N. Hamza, Ukashatu Abubakar, A. I. Tukur, N. A. Wahab

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Modelling of a river involves protracted engagement with uncertainty, thus making developing a reliable model becomes quite cumbersome and often impossible. This paper presents a comparison of a non-linear models based on Radial Basis Function Neural Network (RBFNN) and Hammerstein-Weiner Model (HW) and a conventional linear models, Auto regressive Integrated Moving Average (ARIMA) and Generalized Linear Regression (GLR) models for Kinta River, in Malaysia and Agra station of Yamuna river in India. Experimental data from the rivers were used in validating the models. Simulation results demonstrated that the nonlinear models (RBFNN and HW) averagely increased the performance accuracy of the linear models (ARIMA and GLR) by 20% and 15% at Kinta and Agra station of Yamuna River in the verification phase respectively. Considering the overall results the RBFNN model outperformed the other models with the average increase up to 21% in the verification phase. The model could serves as reliable and useful tool for forecasting the rivers.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781728107844
DOIs
StatePublished - Jun 2019
Externally publishedYes

Publication series

Name2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • ARIMA
  • GLR
  • Kinta River
  • Model
  • RBFNN
  • Yamuna River
  • water quality index

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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