Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Namli, Ersin(1)
• Tanır Kayıkçı, Emine(1)
• Yavuzdoğan, Ahmet(1)
• Ünlü, Ramazan(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
• MakaleAçık Erişim2020Scopus SJRQ1/r/gumushane/13273
  Machine learning and classical forecasting methods based decision support systems for covid-19

  Ünlü, Ramazan; Namli, Ersin

  From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are applied to the novel COVID-19 dataset. According to the results, the Prophet model gives the lowest Root Mean Squared Error (RMSE) score compared to the other three models. Besides, according to this model, a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.

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  AtıfScopus16·WoS12·OpenAlex15

  İncele