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The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Re...

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Main Authors: Yaseen, Zaher, Ehteram, Mohammad, Sharafati, Ahmad, Shahid, Shamsuddin, Al-Ansari, Nadhir, El-Shafie, Ahmed
Format: Article
Published: MDPI 2018
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/20306/
https://doi.org/10.3390/w10091124
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spelling my.um.eprints.203062019-02-14T04:10:53Z http://eprints.um.edu.my/20306/ The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration Yaseen, Zaher Ehteram, Mohammad Sharafati, Ahmad Shahid, Shamsuddin Al-Ansari, Nadhir El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables. MDPI 2018 Article PeerReviewed Yaseen, Zaher and Ehteram, Mohammad and Sharafati, Ahmad and Shahid, Shamsuddin and Al-Ansari, Nadhir and El-Shafie, Ahmed (2018) The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration. Water, 10 (9). p. 1124. ISSN 2073-4441 https://doi.org/10.3390/w10091124 doi:10.3390/w10091124
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Yaseen, Zaher
Ehteram, Mohammad
Sharafati, Ahmad
Shahid, Shamsuddin
Al-Ansari, Nadhir
El-Shafie, Ahmed
The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
description The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.
format Article
author Yaseen, Zaher
Ehteram, Mohammad
Sharafati, Ahmad
Shahid, Shamsuddin
Al-Ansari, Nadhir
El-Shafie, Ahmed
author_facet Yaseen, Zaher
Ehteram, Mohammad
Sharafati, Ahmad
Shahid, Shamsuddin
Al-Ansari, Nadhir
El-Shafie, Ahmed
author_sort Yaseen, Zaher
title The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
title_short The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
title_full The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
title_fullStr The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
title_full_unstemmed The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
title_sort integration of nature-inspired algorithms with least square support vector regression models: application to modeling river dissolved oxygen concentration
publisher MDPI
publishDate 2018
url http://eprints.um.edu.my/20306/
https://doi.org/10.3390/w10091124
_version_ 1643691240497086464
score 13.211869

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