Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Benli, Büsra(1)
• Karatas, Seyda Merve(1)
• Kavuncuoglu, Erhan(1)
• Kavuncuoglu, Hatice(1)
• Sagdic, Osman(1)
• Tanır Kayıkçı, Emine(1)
• Yalcin, Hasan(1)
• Yavuzdoğan, Ahmet(1)

• MakaleYalnız künye2021Scopus SJRQ2/r/gumushane/13316
  A copula approach for sea level anomaly prediction: a case study for the Black Sea

  Yavuzdoğan, Ahmet; Tanır Kayıkçı, Emine

  Forecasting future sea levels is of great importance in terms of the conservation of coastal areas, monitoring and forecasting coastal ecosystems, and the maintenance and planning of coastal structures. In addition, highly accurate sea level forecasts allow adequate water management policies and coastal infrastructures to be developed. Today, many methods, such as harmonic analyses, artificial neural networks and support vector machines, are used to predict sea level anomalies. In this study, a novel approach based on Copula functions is presented for the prediction of sea level anomalies. The primary purpose of this study is to examine the applicability and capability of Copula-based prediction models in predicting short-term variations in the sea level. The minimum 95% correlations and minimum 22 mm RMSE values in sea level anomaly predictions during the testing period indicate that the Copula approach can be a powerful tool in the prediction of sea level anomalies.

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  AtıfScopus12·WoS10·OpenAlex10

  İncele
• MakaleYalnız künye2018Scopus SJRQ3/r/gumushane/15214
  Prediction of the antimicrobial activity of walnut (Juglans regia L.) kernel aqueous extracts using artificial neural network and multiple linear regression

  Kavuncuoglu, Hatice; Kavuncuoglu, Erhan; Karatas, Seyda Merve; Benli, Büsra; Sagdic, Osman; Yalcin, Hasan

  The mathematical model was established to determine the diameter of inhibition zone of the walnut extract on the twelve bacterial species. Type of extraction, concentration, and pathogens were taken as input variables. Two models were used with the aim of designing this system. One of them was developed with artificial neural networks (ANN), and the other was formed with multiple linear regression (MLR). Four common training algorithms were used. Levenberg–Marquardt (LM), Bayesian regulation (BR), scaled conjugate gradient (SCG) and resilient back propagation (RP) were investigated, and the algorithms were compared. Root mean squared error and correlation coefficient were evaluated as performance criteria. When these criteria were analyzed, ANN showed high prediction performance, while MLR showed low prediction performance. As a result, it is seen that when the different input values are provided to the system developed with ANN, the most accurate inhibition zone (IZ) estimates were obtained. The results of this study could offer new perspectives, particularly in the field of microbiology, because these could be applied to other type of extraction, concentrations, and pathogens, without resorting to experiments.

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  AtıfScopus39·WoS33·OpenAlex46

  İncele