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 LU
; Shahid, Shamsuddin
and Abdulkadir, Rabiu Aliyu
(2021)
In Environmental Science and Pollution Research
28.
p.32564-32579
- Abstract
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,... (More)
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.
(Less)
- author
- Pham, Quoc Bao
; Sammen, Saad Sh
; Abba, Sani Isa
; Mohammadi, Babak
LU
; Shahid, Shamsuddin
and Abdulkadir, Rabiu Aliyu
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Flower pollination algorithm, Hybrid models, Phycocyanin pigment concentration, Prediction models, Relevance vector machine
- in
- Environmental Science and Pollution Research
- volume
- 28
- pages
- 32564 - 32579
- publisher
- Springer
- external identifiers
-
- pmid:33625698
- scopus:85101494250
- ISSN
- 0944-1344
- DOI
- 10.1007/s11356-021-12792-2
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
- id
- 4cf815cc-e0a5-4114-88a4-cc814f85fd51
- date added to LUP
- 2021-03-15 09:43:24
- date last changed
- 2024-07-11 11:19:11
@article{4cf815cc-e0a5-4114-88a4-cc814f85fd51,
abstract = {{<p>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.</p>}},
author = {{Pham, Quoc Bao and Sammen, Saad Sh and Abba, Sani Isa and Mohammadi, Babak and Shahid, Shamsuddin and Abdulkadir, Rabiu Aliyu}},
issn = {{0944-1344}},
keywords = {{Flower pollination algorithm; Hybrid models; Phycocyanin pigment concentration; Prediction models; Relevance vector machine}},
language = {{eng}},
pages = {{32564--32579}},
publisher = {{Springer}},
series = {{Environmental Science and Pollution Research}},
title = {{A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation}},
url = {{http://dx.doi.org/10.1007/s11356-021-12792-2}},
doi = {{10.1007/s11356-021-12792-2}},
volume = {{28}},
year = {{2021}},
}