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

Environmental science and pollution research international, 2021-07, Vol.28 (25), p.32564-32579 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021. ;ISSN: 0944-1344 ;EISSN: 1614-7499 ;DOI: 10.1007/s11356-021-12792-2 ;PMID: 33625698

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Title:
A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
Author: Pham, Quoc Bao ; Sammen, Saad Sh ; Abba, Sani Isa ; Mohammadi, Babak ; Shahid, Shamsuddin ; Abdulkadir, Rabiu Aliyu
Subjects: Algorithms ; Annan naturresursteknik ; Aquatic Pollution ; Artificial intelligence ; Artificial neural networks ; Atmospheric Protection/Air Quality Control/Air Pollution ; Chlorophyll ; Contamination ; Cyanobacteria ; Earth and Environmental Science ; Earth and Related Environmental Sciences ; Ecotoxicology ; Engineering and Technology ; Environment ; Environmental Chemistry ; Environmental Engineering ; Environmental Health ; Environmental Sciences ; Flower pollination algorithm ; Geovetenskap och miljövetenskap ; Hybrid models ; Machine learning ; Miljövetenskap ; Monitoring ; Multilayer perceptrons ; Natural Sciences ; Naturresursteknik ; Naturvetenskap ; Neural networks ; Nonlinear systems ; Other Environmental Engineering ; Performance evaluation ; Phycocyanin ; Phycocyanin pigment concentration ; Pollination ; Prediction models ; Relevance vector machine ; Research Article ; Stations ; Support vector machines ; Teknik ; Toxins ; Waste Water Technology ; Water Management ; Water Pollution Control ; Water resources ; Water resources management
Is Part Of: Environmental science and pollution research international, 2021-07, Vol.28 (25), p.32564-32579
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.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 0944-1344
EISSN: 1614-7499
DOI: 10.1007/s11356-021-12792-2
PMID: 33625698
Source: ProQuest Central

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

The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021. ;ISSN: 0944-1344 ;EISSN: 1614-7499 ;DOI: 10.1007/s11356-021-12792-2 ;PMID: 33625698

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