Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

SWT:

Sea water temperature

ANFIS:

Adaptive neuro-fuzzy inference system

FCM:

Fuzzy c-means

GP:

Grid partition

SC:

Subtractive clustering

LSTM:

Long short-term memory

MAE:

Mean absolute error

RMSE:

Root-mean-square error

R :

Correlation coefficient

MFs:

Membership functions

A, B :

Fuzzy clusters

f :

Single output

x, y :

Inputs

p, q, r :

Architectural parameters

J :

Objective function

c :

Cluster center

D :

Number of data points

N :

Number of data clusters

M :

Total number of separated fuzzy subsets

h :

Output

e :

Exponent

\(\odot\) :

Hadamard product

µ :

Cluster membership value

\(\sigma\) :

Gate activation function

m :

Fuzzy partition matrix exponent

n :

Dimension

i :

Input gate

f :

Forget gate

g :

Cell candidate

o :

Output

t :

Time step

References

  • Abyaneh HZ, Nia AM, Varkeshi MB, Marofi S, Kisi O (2011) Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration. J Irrig Drain Eng 137(5):280–286

    Article  Google Scholar 

  • Aparna SG, D’souza S, Arjun NB (2018) Prediction of daily sea surface temperature using artificial neural networks. Int J Remote Sens 39(12):4214–4231. https://doi.org/10.1080/01431161.2018.1454623

    Article  Google Scholar 

  • Arslan N, Sekertekin A (2019) Application of long short-term memory neural network model for the reconstruction of MODIS land surface temperature images. J Atmos Solar-Terrestrial Phys 105100:105100 https://doi.org/10.1016/J.Jastp.2019.105100

  • Attia SI (2015) The influence of condenser cooling water temperature on the thermal efficiency of a nuclear power plant. Ann Nucl Energy 80:371–378

    Article  Google Scholar 

  • Balluff S, Bendfeld J, Krauter S (2015) Short term wind and energy prediction for offshore wind farms using neural networks. In: 2015 International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, Palermo 379–382. https://doi.org/10.1109/Icrera.2015.7418440

  • Bamisile O, Oluwasanmi A, Obiora S, Osei-Mensah E, Asoronye G, Huang Q (2020) Application of deep learning for solar irradiance and solar photovoltaic multi-parameter forecast. Energy Sources, Part A Recovery Utili Environ Eff. https://doi.org/10.1080/15567036.2020.1801903

    Article  Google Scholar 

  • Benmouiza K, Cheknane A (2019) Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoret Appl Climatol 137:31–43

    Article  Google Scholar 

  • Chen J, Zeng G-Q, Zhou W, Du W, Lu K (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manag 165:681–695. https://doi.org/10.1016/J.Enconman.2018.03.098

    Article  Google Scholar 

  • Cobaner M, Citakoglu H, Kisi O, Haktanir T (2014) Estimation of mean monthly air temperatures in Turkey. Comput Electron Agric 109:71–79. https://doi.org/10.1016/J.Compag.2014.09.007

    Article  Google Scholar 

  • Darmawan N, Yuwono T (2019) Effect of increasing sea water temperature on performance of steam turbine of Muara Tawar Power Plant. J Technol Sci 30(2):2088–2033 (PISSN: 0853-4098)

    Google Scholar 

  • Durmayaz A, Sogut OS (2006) Influence of cooling water temperature on the efficiency of a pressurized-water reactor nuclear-power plant. Int J Energy Res 30:799–810. https://doi.org/10.1002/Er.1186

    Article  Google Scholar 

  • Erduman A (2020) A smart short-term solar power output prediction by artificial neural network. Electr Eng 102(3):1441–1449

    Article  Google Scholar 

  • Feigl M, Lebiedzinski K, Herrnegger M, Schulz K (2021) Machine-learning methods for stream water temperature prediction. Hydrol Earth Syst Sci 25:2951–2977. https://doi.org/10.5194/hess-25-2951-2021

    Article  Google Scholar 

  • Graf R, Aghelpour P (2021) Daily river water temperature prediction: a comparison between neural network and stochastic techniques. Atmosphere 12(9):1154

    Article  Google Scholar 

  • Hey-Min C, Min-Kyu K, Hyun Y (2021) Abnormally high water temperature prediction using LSTM deep learning model. J Intell Fuzzy Syst 40(4):8013–8020

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  • Karakuş O, Kuruoǧlu EE, Altinkaya MA (2017) One-day ahead wind speed/power prediction based on polynomial autoregressive model. IET Renew Power Gener 11(11):1430–1439

    Article  Google Scholar 

  • Kim BK, Jeong YH (2013) High cooling water temperature effects on design and operational safety of NPPS in the Gulf Region. Nucl Eng Technol 45:961–968. https://doi.org/10.5516/net.03.2012.079

  • Kim M, Yang H, Kim J (2020) Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model. Remote Sens 12:3654. https://doi.org/10.3390/Rs12213654

    Article  Google Scholar 

  • Kisi O, Sanikhani H (2015) Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. Int J Climatol 35:3834–3846. https://doi.org/10.1002/Joc.4249

    Article  Google Scholar 

  • Kisi O, Shiri J (2014) Prediction of long-term monthly air temperature using geographical inputs. Int J Climatol 34:179–186. https://doi.org/10.1002/Joc.3676

    Article  Google Scholar 

  • Liu R, Liu L (2019) Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm. Soft Comput 23(22):11829–11838

    Article  Google Scholar 

  • Liu H, Mi X, Li Y (2018) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers Manag 156:498–514. https://doi.org/10.1016/J.Enconman.2017.11.053

    Article  Google Scholar 

  • López E, Valle C, Allende H, Gil E, Madsen H (2018) Wind power forecasting based on echo state networks and long short-term memory. Energies 11:526. https://doi.org/10.3390/En11030526

    Article  Google Scholar 

  • Mathworks (2020) Fuzzy C-means clustering [Online]. Available: https://www.mathworks.com/help/deeplearning/ug/long-short-term-memory-networks.html. Accessed 28 July 2021

  • Ozbek A, Sekertekin A, Bilgili M, Niyazi A (2021a) Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA. Arab J Geosci 14:622. https://doi.org/10.1007/S12517-021-06982-Y

    Article  Google Scholar 

  • Ozbek A, Yildirim A, Bilgili M (2021b) Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant. Energy Sources Part a: Recovery Util Environ Eff. https://doi.org/10.1080/15567036.2021.1924316

    Article  Google Scholar 

  • Park I, Kim HS, Lee J, Kim JH, Song CH, Kim HK (2019) Temperature prediction using the missing data refinement model based on a long short-term memory neural network. Atmosphere (basel) 10(11):1–16

    Google Scholar 

  • Peng L, Liu S, Liu R, Wang L (2018) Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy 162:1301–1314. https://doi.org/10.1016/J.Energy.2018.05.052

    Article  Google Scholar 

  • Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468. https://doi.org/10.1016/J.Energy.2018.01.177

    Article  Google Scholar 

  • Qu X, Xiaoning K, Chao Z (2016) Short-term prediction of wind power based on deep long short-term memory. In: 2016 IEEE Pes Asia-Pacific Power And Energy Engineering Conference (APPEEC). IEEE 1148–1152

  • Quan Q, Hao Z, Xifeng H, Jingchun L (2020) Research on water temperature prediction based on improved support vector regression. Neural Comput Applic. https://doi.org/10.1007/s00521-020-04836-4

    Article  Google Scholar 

  • Rajesh M, Rehana S (2021) Prediction of river water temperature using machine learning algorithms: a tropical river system of India. J Hydroinf 2(3):605–626. https://doi.org/10.2166/hydro.2021.121

    Article  Google Scholar 

  • Read JS, Jia X, Willard J, Appling AP, Zwart JA, Oliver SK (2019) Process-guided deep learning predictions of lake water temperature. Water Resour Res 55:9173–9190. https://doi.org/10.1029/2019WR024922

    Article  Google Scholar 

  • Salman AG, Heryadi Y, Abdurahman E, Suparta W (2018) Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Comput Sci 135:89–98

    Article  Google Scholar 

  • Samadianfard S, Kazemi H, Kisi O, Liu WC (2016) Water temperature prediction in a subtropical subalpine lake Using Soft Computing Techniques. Earth Sci Res J 20(2):D1–D11

    Article  Google Scholar 

  • Sekertekin A, Bilgili M, Arslan N, Yildirim A, Celebi K, Ozbek A (2021) Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long Ssort-term memory (LSTM) network. Meteorol Atmos Phys 133:943–959. https://doi.org/10.1007/s00703-021-00791-4

    Article  Google Scholar 

  • Stajkowski S, Kumar D, Samui P, Bonakdari H, Gharabaghi B (2020) Genetic-algorithm-optimized sequential model for water temperature prediction. Sustainability 12(13):5374. https://doi.org/10.3390/su12135374

    Article  Google Scholar 

  • Tabari H, Kisi O, Ezani A, Hosseinzadeh TP (2012) SVM, ANFIS, Regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444–445:78–89

    Article  Google Scholar 

  • Wei L, Guan L, Qu L (2020a) Prediction of sea surface temperature in the South China Sea by artificial neural networks. IEEE Geoscı Remote Sens Lett 17:4

    Google Scholar 

  • Wei L, Guan L, Qu L, Guo D (2020b) Prediction of sea surface temperature in the China seas based on long short-term memory neural networks. Remote Sens 12:2697. https://doi.org/10.3390/Rs12172697

    Article  Google Scholar 

  • Wu W, Chen K, Qiao Y, Lu Z (2016) Probabilistic short-term wind power forecasting based on deep neural networks. In: 2016 International Conference on Probabilistic Methods Applied To Power Systems (PMAPS). IEEE, pp 1–8. https://doi.org/10.1109/Pmaps.2016.7764155

  • Yu X et al. (2020) A novel method for sea surface temperature prediction based of deep learning. Hindawi Mathematical Problems in Engineering Article ID: 6387173, p 9 https://doi.org/10.1155/2020/6387173

  • Zahroh S, Hidayat Y, Pontoh RS, Santoso A, Sukono, Bon AT (2019) Modeling and forecasting daily temperature in Bandung. Proc Int Conf Ind Eng Oper Manag (November):406–412

  • Zhang Q, Wang H, Dong J, Zhong G, Sun X (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/Lgrs.2017.2733548

    Article  Google Scholar 

  • Zhang B, Wu JL, Chang PC (2018) A multiple time series-based recurrent neural network for short-term load forecasting. Soft Comput 22(12):4099–4112

    Article  Google Scholar 

  • Zhang J, Cao X, Xie J, Kou P (2019) An improved long short-term memory model for dam displacement prediction. Math Probl Eng 2019:1–14. https://doi.org/10.1155/2019/6792189

    Article  Google Scholar 

  • Zhu S, Nyarko EK, Nyarko MH, Heddam S, Wu S (2019a) Assessing the performance of a suite of machine learning models for daily river water temperature prediction. PeerJ 7:e7065. https://doi.org/10.7717/peerj.7065

    Article  Google Scholar 

  • Zhu S, Heddam S, Nyarko EK, Hadzima-Nyarko M, Piccolroaz S, Wu S (2019b) Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environ Sci Pollut Res 26(1):402–420

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank the Turkish State Meteorological Service for allowing the use of the sea water temperature data in this research.

Funding

The author received financial support from the Office of Scientific Research Projects of Cukurova University for this project under contract no. FBA-2021–14004.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, 01950, Adana, Turkey

    Arif Ozbek

Authors
  1. Arif Ozbek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arif Ozbek.

Ethics declarations

Conflict of interest

The author declares no competing interests.

Additional information

Responsible Editor: Zhihua Zhang

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozbek, A. Prediction of daily sea water temperature in Turkish seas using machine learning approaches. Arab J Geosci 15, 1625 (2022). https://doi.org/10.1007/s12517-022-10893-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-10893-x

Keywords

Search

Navigation