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

doi:10.1109/I2CACIS.2019.8825085

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 proceeding › Conference contribution › peer-review

18 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 language	English
Title of host publication	2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781728107844
DOIs	https://doi.org/10.1109/I2CACIS.2019.8825085
State	Published - Jun 2019
Externally published	Yes

Publication series

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

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

UN SDGs

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

SDG 6 Clean Water and Sanitation

Keywords

ARIMA
GLR
Kinta River
Model
RBFNN
Yamuna River
water quality index

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Optimization

Access to Document

10.1109/I2CACIS.2019.8825085

Cite this

Abba, S. I., Gaya, M. S., Yakubu, M. L., Zango, M. U., Abdulkadir, R. A., Saleh, M. A., Hamza, A. N., Abubakar, U., Tukur, A. I., & Wahab, N. A. (2019). Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches. In 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings (pp. 1-6). Article 8825085 (2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/I2CACIS.2019.8825085

Abba, S. I. ; Gaya, M. S. ; Yakubu, M. L. et al. / Modelling of Uncertain System : A comparison study of Linear and Non-Linear Approaches. 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1-6 (2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings).

@inproceedings{91ea23d8d6d64d6d8a126985cceac094,

title = "Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches",

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.",

keywords = "ARIMA, GLR, Kinta River, Model, RBFNN, Yamuna River, water quality index",

author = "Abba, \{S. I.\} and Gaya, \{M. S.\} and Yakubu, \{M. L.\} and Zango, \{M. U.\} and Abdulkadir, \{R. A.\} and Saleh, \{M. A.\} and Hamza, \{A. N.\} and Ukashatu Abubakar and Tukur, \{A. I.\} and Wahab, \{N. A.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = jun,

doi = "10.1109/I2CACIS.2019.8825085",

language = "English",

series = "2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--6",

booktitle = "2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings",

address = "United States",

}

Abba, SI, Gaya, MS, Yakubu, ML, Zango, MU, Abdulkadir, RA, Saleh, MA, Hamza, AN, Abubakar, U, Tukur, AI & Wahab, NA 2019, Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches. in 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings., 8825085, 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1-6. https://doi.org/10.1109/I2CACIS.2019.8825085

Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches. / Abba, S. I.; Gaya, M. S.; Yakubu, M. L. et al.
2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1-6 8825085 (2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings).

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

TY - GEN

T1 - Modelling of Uncertain System

T2 - A comparison study of Linear and Non-Linear Approaches

AU - Abba, S. I.

AU - Gaya, M. S.

AU - Yakubu, M. L.

AU - Zango, M. U.

AU - Abdulkadir, R. A.

AU - Saleh, M. A.

AU - Hamza, A. N.

AU - Abubakar, Ukashatu

AU - Tukur, A. I.

AU - Wahab, N. A.

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - ARIMA

KW - GLR

KW - Kinta River

KW - Model

KW - RBFNN

KW - Yamuna River

KW - water quality index

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U2 - 10.1109/I2CACIS.2019.8825085

DO - 10.1109/I2CACIS.2019.8825085

M3 - Conference contribution

AN - SCOPUS:85072911965

T3 - 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings

SP - 1

EP - 6

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Abba SI, Gaya MS, Yakubu ML, Zango MU, Abdulkadir RA, Saleh MA et al. Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches. In 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1-6. 8825085. (2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings). doi: 10.1109/I2CACIS.2019.8825085

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

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this