Geomatics and Environmental Engineering, vol. 17, no. 2

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Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

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Submitted
Aug 17, 2021
Accepted
Nov 22, 2022
Published
Jan 30, 2023
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Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

https://doi.org/10.7494/geom.2023.17.2.31
Salah Arif
Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria
https://orcid.org/0000-0002-8341-9688
Adel Djellal
Higher School for Industrial Technology, Energy Systems Technology Research Laboratory, Annaba, Algeria
https://orcid.org/0000-0002-5723-0099
Nawel Djebbari
Chadli Bendjedid University, Department of Marine Biology, El Tarf, Algeria
https://orcid.org/0000-0002-4851-9093
Saber Belhaoues
Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria
https://orcid.org/0000-0001-6899-7431
Hassen Touati
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
Fatma Guellati
Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria
https://orcid.org/0000-0001-7237-4341
Mourad Bensouilah
Badji Mokhtar University, Ecobiology Laboratory for Marine Environments and Coastal Areas, Annaba, Algeria
https://orcid.org/0000-0002-9574-5915

Corresponding Author(s) : Salah Arif

arifsalah23@hotmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

Abstract

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.

Keywords

Microcystis cell density Multiple Linear Regression Support Vector Machine Particle Swarm Optimization Genetic Algorithm Bird Swarm Algorithm
Arif, S., Djellal, A., Djebbari, N. ., Belhaoues, S. ., Touati, H. ., Guellati, F. Z., & Bensouilah, M. (2023). Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming. Geomatics and Environmental Engineering, 17(2), 31–68. https://doi.org/10.7494/geom.2023.17.2.31
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