Babak Mohammadi
Doktorand
A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
Författare
Summary, in English
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.
Avdelning/ar
- Institutionen för naturgeografi och ekosystemvetenskap
Publiceringsår
2021
Språk
Engelska
Sidor
32564-32579
Publikation/Tidskrift/Serie
Environmental Science and Pollution Research
Volym
28
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Environmental Sciences
- Other Environmental Engineering
Nyckelord
- Flower pollination algorithm
- Hybrid models
- Phycocyanin pigment concentration
- Prediction models
- Relevance vector machine
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0944-1344