Determining the Level of Bacteriological Pollution Level in Gelevera Creek, Giresun

Akkan, Tamer; Çolaker, Fatmagül

doi:10.35229/jaes.818132

“…Antibiotic treatment has been applied for many years against bacterial infections in aquaculture (Done et al, 2015). The extensive use of antibiotics causes the change of microbiota in aquaculture and can led to development of antibiotic resistant bacteria (Akkan & Çolaker, 2020;Akkan & Topkaraoğlu, 2019;2017;2019;Balta, 2020;Resende et al, 2012). A novel alternative to antimicrobial therapy, boric acid can be recommended for treatment of bacterial infections.…”

Section: Introductionmentioning

confidence: 99%

Antibacterial Effects of Boric Acid Against Aquatic Pathogens

Yaylacı

¹

2021

Journal of Anatolian Environmental and Animal Sciences

1

0

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Boron is a bioactive trace element generally found in rock, soil, water and air. It is an essential micronutrient for plants besides having beneficial effects on biological functions of human and animal health. Boric acid has antibacterial properties. Thus, the aim of this study was to evaluate the possible bactericidal and bacteriostatic effects of boric acid on the aquatic pathogens: Aeromonas veronii, Photobacterium damselae subsp. damselae, Vibrio anguillarum, Vibrio vulnificus, Vibrio harveyi, Vibrio rotiferianus, Vibrio tubiashii, Vibrio parahaemolyticus, Vibrio furnissii and Vibrio fluvialis. The inhibitory properties of boric acid were detected by agar well diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) methods. The results showed that Photobacterium damselae subsp. damselae and Vibrio species were resistant to different concentrations of boric acid. However, boric acid showed an inhibitory effect against Aeromonas veronii at concentrations of 3.09 and 1.54 mg/ml and zone diameters on the agar were measured as 19.4 ± 0.5, 15.92 ± 0.6 mm, respectively. The MIC and MBC of boric acid for Aeromonas veronii were found to be effective at a concentration of 1.54 mg/ml. These results suggested that boric acid might serve as a potential antimicrobial agent for Aeromonas veronii in aquaculture.

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Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

Isık,

Akkan

2024

Arab J Sci Eng

3

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Identifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.

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Application of a Novel Method for Predicting Water Quality Using the Pi-Sigma Neural Network: Gelevera Creek (Giresun, Türkiye)

Işık

¹

,

Akkan

²

2022

Preprint

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Identifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating WQI. In this context, water samples have been collected in monthly from 8 stations of Gelevera Creek. The traditional evaluation with WQI of Gelevera Creek was calculated as average so good WQ. The novel application which is the Single multiplicative neuron (SMN) model, multilayer perceptron and pi-sigma artificial neural networks (PS-ANNs) are applied for predicting of WQI. SMN model and PS-ANNs are firstly used for modelling of WQ in the literature. It is noted that the best results of Gelevera Creek were obtained with the PS-ANN. As a result of, it is suggested to obtain the WQI with the proposed optimum PS-ANN instead of using calculation methods such as WQI that include long calculations.

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Determining the Level of Bacteriological Pollution Level in Gelevera Creek, Giresun

Cited by 6 publications

References 11 publications

Antibacterial Effects of Boric Acid Against Aquatic Pathogens

Antibacterial Effects of Boric Acid Against Aquatic Pathogens

Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

Application of a Novel Method for Predicting Water Quality Using the Pi-Sigma Neural Network: Gelevera Creek (Giresun, Türkiye)

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