A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation

Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measur...

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Main Authors: Pham, Quoc Bao, Sammen, Saad Sh., Abba, Sani Isa, Mohammadi, Babak, Shahid, Shamsuddin, Abdulkadir, Rabiu Aliyu
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2021
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Online Access:http://eprints.utm.my/id/eprint/94122/
http://dx.doi.org/10.1007/s11356-021-12792-2
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spelling my.utm.941222022-02-28T13:32:18Z http://eprints.utm.my/id/eprint/94122/ A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation Pham, Quoc Bao Sammen, Saad Sh. Abba, Sani Isa Mohammadi, Babak Shahid, Shamsuddin Abdulkadir, Rabiu Aliyu TA Engineering (General). Civil engineering (General) Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources. Springer Science and Business Media Deutschland GmbH 2021-07 Article PeerReviewed Pham, Quoc Bao and Sammen, Saad Sh. and Abba, Sani Isa and Mohammadi, Babak and Shahid, Shamsuddin and Abdulkadir, Rabiu Aliyu (2021) A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environmental Science and Pollution Research, 28 (25). pp. 32564-32579. ISSN 0944-1344 http://dx.doi.org/10.1007/s11356-021-12792-2 DOI:10.1007/s11356-021-12792-2
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Pham, Quoc Bao
Sammen, Saad Sh.
Abba, Sani Isa
Mohammadi, Babak
Shahid, Shamsuddin
Abdulkadir, Rabiu Aliyu
A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
description Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
format Article
author Pham, Quoc Bao
Sammen, Saad Sh.
Abba, Sani Isa
Mohammadi, Babak
Shahid, Shamsuddin
Abdulkadir, Rabiu Aliyu
author_facet Pham, Quoc Bao
Sammen, Saad Sh.
Abba, Sani Isa
Mohammadi, Babak
Shahid, Shamsuddin
Abdulkadir, Rabiu Aliyu
author_sort Pham, Quoc Bao
title A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
title_short A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
title_full A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
title_fullStr A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
title_full_unstemmed A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
title_sort new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/94122/
http://dx.doi.org/10.1007/s11356-021-12792-2
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