Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

• Autorzy: Arif Salah , Djellal Adel , Djebbari Nawel , Belhaoues Saber , Touati Hassen , Guellati Fatma Zohra , Bensouilah Mourad

Identyfikatory

DOI

10.7494/geom.2023.17.2.31

• Języki publikacji: EN

Abstrakty

EN

Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.

Słowa kluczowe

EN

microcystis cell density; Multiple Linear Regression; Support Vector Machine; Particle Swarm Optimization; genetic algorithm; Bird Swarm Algorithm

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2023
• Tom: Vol. 17, no. 2
• Strony: 31--68
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Arif Salah

• Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria

• https://orcid.org/0000-0002-8341-9688

autor

Djellal Adel

• Higher School for Industrial Technology, Energy Systems Technology Research Laboratory, Annaba, Algeria

• https://orcid.org/0000-0002-5723-0099

autor

Djebbari Nawel

• Chadli Bendjedid University, Department of Marine Biology, El Tarf, Algeria

• https://orcid.org/0000-0002-4851-9093

autor

Belhaoues Saber

• Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria

• https://orcid.org/0000-0001-6899-7431

autor

Touati Hassen

• University of 8 Mai 1945 Guelma, Faculty of Natural and Life Sciences and Earth and Universe Sciences, Department of Biology, Guelma, Algeria

• https://orcid.org/0000-0002-4185-5449

autor

Guellati Fatma Zohra

• Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria

• https://orcid.org/0000-0001-7237-4341

autor

Bensouilah Mourad

• Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria

• https://orcid.org/0000-0002-9574-5915

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)