Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River

Recent approaches toward solving the regression problems which are characterized by dynamic and nonlinear pattern such as machine learning modeling (including artificial intelligence (AI) approaches) have proven to be useful and successful tools for prediction. Approaches that integrate predictive m...

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Main Authors:	Jing, Li, Husam Ali , Abdulmohsin, Samer Sami , Hasan, Li , Kaiming, Belal , Al-Khateeb, Mazen Ismaeel, Ghareb, Mohammed, Muamer N.
Format:	Article
Language:	English English
Published:	Springer 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/1/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River.pdf http://umpir.ump.edu.my/id/eprint/18339/2/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River%201.pdf

id	ump-18339
recordtype	eprints
spelling	ump-183392018-01-30T05:54:58Z http://umpir.ump.edu.my/id/eprint/18339/ Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River Jing, Li Husam Ali , Abdulmohsin Samer Sami , Hasan Li , Kaiming Belal , Al-Khateeb Mazen Ismaeel, Ghareb Mohammed, Muamer N. QA76 Computer software Recent approaches toward solving the regression problems which are characterized by dynamic and nonlinear pattern such as machine learning modeling (including artificial intelligence (AI) approaches) have proven to be useful and successful tools for prediction. Approaches that integrate predictive model with optimization algorithm such as hybrid soft computing have resulted in the enhancement of the accuracy and preciseness of models during problem predictions. In this research, the implementation of hybrid evolutionary model based on integrated support vector regression (SVR) with firefly algorithm (FFA) was investigated for water quality indicator prediction. The monthly water quality indicator (WQI) that was used to test the hybrid model over a period of 10 years belongs to the Euphrates River, Iraq. The use of the WQI as an application for this research was stimulated based on the fact that WQI is usually calculated using a manual formulation which takes much time, efforts and occasionally may be associated with errors that were not intended during the subindex calculations. The parameters considered during the formulation of the prediction model were water quality parameters as input and WQI as output. The SVR model was used to verify the accuracy of the inspected SVR–FFA model. Different statistical metrics such as best fit of goodness and absolute error measures were used to evaluate the model. The performance of the hybrid model in recognizing the dynamic and nonlinear pattern characteristics was high and remarkable compared to the pure model. The SVR–FFA model was also demonstrated to be a good and robust soft computing technique toward the prediction of WQI. The proposed model enhanced the absolute error measurements (e.g., root mean square error and mean absolute error) over the SVR-based model by 42 and 58%, respectively. Springer 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18339/1/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18339/2/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River%201.pdf Jing, Li and Husam Ali , Abdulmohsin and Samer Sami , Hasan and Li , Kaiming and Belal , Al-Khateeb and Mazen Ismaeel, Ghareb and Mohammed, Muamer N. (2017) Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River. Neural Computing and Applications. pp. 1-11. ISSN 0941-0643 https://link.springer.com/article/10.1007/s00521-017-3112-7 DOI: 10.1007/s00521-017-3112-7
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Jing, Li Husam Ali , Abdulmohsin Samer Sami , Hasan Li , Kaiming Belal , Al-Khateeb Mazen Ismaeel, Ghareb Mohammed, Muamer N. Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
description	Recent approaches toward solving the regression problems which are characterized by dynamic and nonlinear pattern such as machine learning modeling (including artificial intelligence (AI) approaches) have proven to be useful and successful tools for prediction. Approaches that integrate predictive model with optimization algorithm such as hybrid soft computing have resulted in the enhancement of the accuracy and preciseness of models during problem predictions. In this research, the implementation of hybrid evolutionary model based on integrated support vector regression (SVR) with firefly algorithm (FFA) was investigated for water quality indicator prediction. The monthly water quality indicator (WQI) that was used to test the hybrid model over a period of 10 years belongs to the Euphrates River, Iraq. The use of the WQI as an application for this research was stimulated based on the fact that WQI is usually calculated using a manual formulation which takes much time, efforts and occasionally may be associated with errors that were not intended during the subindex calculations. The parameters considered during the formulation of the prediction model were water quality parameters as input and WQI as output. The SVR model was used to verify the accuracy of the inspected SVR–FFA model. Different statistical metrics such as best fit of goodness and absolute error measures were used to evaluate the model. The performance of the hybrid model in recognizing the dynamic and nonlinear pattern characteristics was high and remarkable compared to the pure model. The SVR–FFA model was also demonstrated to be a good and robust soft computing technique toward the prediction of WQI. The proposed model enhanced the absolute error measurements (e.g., root mean square error and mean absolute error) over the SVR-based model by 42 and 58%, respectively.
format	Article
author	Jing, Li Husam Ali , Abdulmohsin Samer Sami , Hasan Li , Kaiming Belal , Al-Khateeb Mazen Ismaeel, Ghareb Mohammed, Muamer N.
author_facet	Jing, Li Husam Ali , Abdulmohsin Samer Sami , Hasan Li , Kaiming Belal , Al-Khateeb Mazen Ismaeel, Ghareb Mohammed, Muamer N.
author_sort	Jing, Li
title	Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
title_short	Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
title_full	Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
title_fullStr	Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
title_full_unstemmed	Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
title_sort	hybrid soft computing approach for determining water quality indicator: euphrates river
publisher	Springer
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/1/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River.pdf http://umpir.ump.edu.my/id/eprint/18339/2/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River%201.pdf
first_indexed	2023-09-18T22:25:56Z
last_indexed	2023-09-18T22:25:56Z
_version_	1777415965800136704

Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River

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