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

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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, 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.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding or first author (Dr. Saad Sh. Sammen, Saad123engineer@yahoo.com, or Quoc Bao Pham, phambaoquoc@duytan.edu.vn) upon reasonable request.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  2. Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  3. Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Diyala Governorate, Iraq

    Saad Sh. Sammen

  4. Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria

    Sani Isa Abba

  5. Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62, Lund, Sweden

    Babak Mohammadi

  6. Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Shamsuddin Shahid

  7. Department of Electrical and Electronic, Kano University of Science and Technology, Wudil, Nigeria

    Rabiu Aliyu Abdulkadir

  8. Department of Computer Science, Kano University of Science and Technology, Wudil, Nigeria

    Rabiu Aliyu Abdulkadir

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  1. Quoc Bao Pham
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  2. Saad Sh. Sammen
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  3. Sani Isa Abba
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  4. Babak Mohammadi
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  5. Shamsuddin Shahid
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  6. Rabiu Aliyu Abdulkadir
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Contributions

Quoc Bao Pham: conceptualization, formal analysis, writing, editing, review, and supervision. Saad Sh. Sammen: data collection, writing and reviewing the manuscript, conceptual and supervision. Babak Mohammadi: modeling, writing, and reviewing the manuscript. S.I. Abba and R.A. Abdulkadir: manuscript writing, data, and visualization. Shamsuddin Shahid: formal analysis, writing, and reviewing the manuscript.

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Correspondence to Saad Sh. Sammen.

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Pham, Q.B., Sammen, S.S., Abba, S.I. et al. A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environ Sci Pollut Res 28, 32564–32579 (2021). https://doi.org/10.1007/s11356-021-12792-2

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