Prediction of daily sea water temperature in Turkish seas using machine learning approaches


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Özbek A.

ARABIAN JOURNAL OF GEOSCIENCES, vol.15, no.1625, pp.1-19, 2022 (Scopus)

  • Publication Type: Article / Article
  • Volume: 15 Issue: 1625
  • Publication Date: 2022
  • Doi Number: 10.1007/s12517-022-10893-x
  • Journal Name: ARABIAN JOURNAL OF GEOSCIENCES
  • Journal Indexes: Scopus, Aquatic Science & Fisheries Abstracts (ASFA), Geobase, INSPEC
  • Page Numbers: pp.1-19
  • Çukurova University Affiliated: Yes

Abstract

In coal-fired or nuclear power plants installed at the seaside, turbine efficiency is directly dependent on sea water temperature

(SWT). Given the long-term average climatic conditions, the cooling medium temperature plays an important role in

the design of any power plant. Therefore, the efficiency in electricity generation is significantly affected as instantaneous

changes in seawater temperature will cause the cooling environment design temperature of the plant to deviate. In this

respect, accurate SWT estimation plays an important role for electrical output from power plant applications. In this study,

various machine learning approaches namely fuzzy C-means clustering (FCM), grid partition (GP), subtractive clustering

(SC)-based adaptive neuro-fuzzy inference system (ANFIS), and long short-term memory (LSTM) neural network were used

to make one-day ahead SWT predictions. Analyses were made using 5-year daily mean SWTs measured by Turkish State

Meteorological Service between 2014 and 2018 at 3 different stations (Mersin, Izmir, and Samsun provinces) on the coasts

of the country. Mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R) were used as

evaluation criteria. According to the daily SWT prediction, the best results for MAE, RMSE, and R values were obtained

for Mersin as 0.1003 °C, 0.1654 °C, and 0.999594, respectively, with ANFIS-GP model; for Izmir as 0.1754 °C, 0.2638 °C,

and 0.998962, respectively, with ANFIS-SC model; and for Samsun as 0.2716 °C, 0.3629 °C, and 0.998285, respectively,

with LSTM model.