Application of artificial neural networks for water quality prediction

A. Najah, A. El-Shafie, Othman A. Karim, Amr H. El-Shafie

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

The term "water quality" is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

Original languageEnglish
Pages (from-to)187-201
Number of pages15
JournalNeural Computing and Applications
Volume22
Issue numberSUPPL.1
DOIs
Publication statusPublished - May 2013

Fingerprint

Water quality
Neural networks
Rivers
Water resources
Linear regression
Catchments
Multilayer neural networks
Surface waters
Pollution
Blood
Processing
Water

Keywords

  • Multilayer perceptron
  • Radial basis function
  • Water quality prediction model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Application of artificial neural networks for water quality prediction. / Najah, A.; El-Shafie, A.; A. Karim, Othman; El-Shafie, Amr H.

In: Neural Computing and Applications, Vol. 22, No. SUPPL.1, 05.2013, p. 187-201.

Research output: Contribution to journalArticle

Najah, A. ; El-Shafie, A. ; A. Karim, Othman ; El-Shafie, Amr H. / Application of artificial neural networks for water quality prediction. In: Neural Computing and Applications. 2013 ; Vol. 22, No. SUPPL.1. pp. 187-201.
@article{349ddbfdef624cf7af0427abe2638e6c,
title = "Application of artificial neural networks for water quality prediction",
abstract = "The term {"}water quality{"} is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.",
keywords = "Multilayer perceptron, Radial basis function, Water quality prediction model",
author = "A. Najah and A. El-Shafie and {A. Karim}, Othman and El-Shafie, {Amr H.}",
year = "2013",
month = "5",
doi = "10.1007/s00521-012-0940-3",
language = "English",
volume = "22",
pages = "187--201",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "SUPPL.1",

}

TY - JOUR

T1 - Application of artificial neural networks for water quality prediction

AU - Najah, A.

AU - El-Shafie, A.

AU - A. Karim, Othman

AU - El-Shafie, Amr H.

PY - 2013/5

Y1 - 2013/5

N2 - The term "water quality" is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

AB - The term "water quality" is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

KW - Multilayer perceptron

KW - Radial basis function

KW - Water quality prediction model

UR - http://www.scopus.com/inward/record.url?scp=84878011320&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878011320&partnerID=8YFLogxK

U2 - 10.1007/s00521-012-0940-3

DO - 10.1007/s00521-012-0940-3

M3 - Article

VL - 22

SP - 187

EP - 201

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - SUPPL.1

ER -