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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| Format: | Article |
| Language: | English |
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MDPI AG
2018
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| Online Access: | http://eprints.utm.my/79681/1/ShamsuddinShahid2018_TheIntegrationofNatureInspiredAlgorithms.pdf |
| _version_ | 1796863089169137664 |
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| author | Yaseen, Z. M. Ehteram, M. Sharafati, A. Shahid, S. Al-Ansari, N. El-Shafie, A. |
| author_facet | Yaseen, Z. M. Ehteram, M. Sharafati, A. Shahid, S. Al-Ansari, N. El-Shafie, A. |
| author_sort | Yaseen, Z. M. |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T20:21:27Z |
| format | Article |
| id | utm.eprints-79681 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:21:27Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-796812019-01-28T04:59:00Z http://eprints.utm.my/79681/ The integration of nature-inspired algorithms with Least Square Support Vector regression models: application to modeling river dissolved oxygen concentration Yaseen, Z. M. Ehteram, M. Sharafati, A. Shahid, S. Al-Ansari, N. El-Shafie, A. 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 AG 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/79681/1/ShamsuddinShahid2018_TheIntegrationofNatureInspiredAlgorithms.pdf Yaseen, Z. M. and Ehteram, M. and Sharafati, A. and Shahid, S. and Al-Ansari, N. and El-Shafie, A. (2018) The integration of nature-inspired algorithms with Least Square Support Vector regression models: application to modeling river dissolved oxygen concentration. Water (Switzerland), 10 (9). ISSN 2073-4441 http://dx.doi.org/10.3390/w10091124 DOI:10.3390/w10091124 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Yaseen, Z. M. Ehteram, M. Sharafati, A. Shahid, S. Al-Ansari, N. El-Shafie, A. The integration of nature-inspired algorithms with Least Square Support Vector regression models: application to modeling river dissolved oxygen concentration |
| title | 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_short | 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 |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/79681/1/ShamsuddinShahid2018_TheIntegrationofNatureInspiredAlgorithms.pdf |
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