Evaluation of Deep Sea Discharge Systems Efficiency in the Eastern Black Sea Using Artificial Neural Network: a Case Study for Trabzon, Turkey

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Aydın Er B., ODABAŞ M. S., Senyer N., Ardalı Y.

BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY, vol.65, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 65
  • Publication Date: 2022
  • Doi Number: 10.1590/1678-4324-2022210397
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: Artificial Neural Network, Black Sea, Deep Sea Discharge, Total Coliform, Trabzon, OUTFALL
  • Ondokuz Mayıs University Affiliated: Yes


The aim of this study is to evaluate the parameters such as pH, dissolved oxygen, temperature, conductivity, salinity, biological oxygen demand (BOD), total suspended solid, ammonia, chlorophyll-a and heavy metals affecting total coliform values in seawater using Artificial Neural Network (ANN) modelling at the Eastern Black Sea coast of Turkey. The results obtained from ANN model were compared with actual total coliform values. The samples were taken from the different points selected along the deep sea discharge systems starting from the diffuser end of three domestic deep sea discharge systems at Turkey's Eastern Black Sea coast. ANN model was developed for estimating the relationship between total coliform and other parameters. The parameters measured in seawater samples were analyzed by using the ANN model for prediction of coliform values. The results showed that neural network model is capable of estimating the sea pollution with a reasonable accuracy.