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Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach

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Abstract

High temporal resolution remote sensing images provide continuous data about the marine environment, which is critical for gaining extensive knowledge about the aquatic environment and marine species. Sea surface temperature (SST) is one of the basic parameters that can be obtained with the help of remote sensing. Long-term alterations in the SST can affect the aquatic environment and marine species, such as the life expectancy of anchovies in the Black Sea. Forecasting the dynamics of SSTs is crucial for detecting and eliminating the SST-oriented impacts. The goal of the current study is to construct a predictive model to estimate the daily SST value for the mid-Black Sea using a machine learning approach by employing time-series satellite data from 2008 to 2021. Turkey’s mid-Black Sea coastal line, comprising Ordu, Samsun, and Sinop stations, was chosen as the study area. The SST predictive model was represented by applying the recurrent neural network (RNN) long- and short-term memory (LSTM). Adam stochastic optimization was used for validation, and the mean square error (MSE) for each location was found to be 0.914, 0.815, and 0.802, respectively. The findings indicate that our model is significantly promising for accurate and effective short- and midterm daily SST prediction.

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Data availability

The data of the study will be provided upon request.

Code availability

The data of the study will be provided upon request.

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Acknowledgements

This research was conducted as a part Studio IIA lecture by Remote Sensing and Geographical Information System Department in Eskisehir Technical University. The authors would like to express their sincere gratitude to Prof. Dr. Saye Nihan Çabuk for the continuous support during the study. The data and code used in this research will be provided to interested researchers upon request.

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Authors and Affiliations

  1. bePetro Consultancy, Çankaya, Ankara, Turkey

    Hakan Oktay Aydınlı

  2. Computer Research and Application Center, Eskişehir Anadolu University, Eskişehir, Turkey

    Ali Ekincek

  3. Turkish Naval Forces Çankaya, Ankara, Turkey

    Mervegül Aykanat-Atay

  4. Titan2 IC İçtas Joint Venture, Gülnar, Mersin, Turkey

    Berkan Sarıtaş

  5. Graduate School of Sciences, Eskişehir Technical University, Remote Sensing and Geographic Information Systems, Eskişehir, Turkey

    Hakan Oktay Aydınlı, Ali Ekincek, Mervegül Aykanat-Atay, Berkan Sarıtaş & Mehtap Özenen-Kavlak

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  1. Hakan Oktay Aydınlı
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  3. Mervegül Aykanat-Atay
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  4. Berkan Sarıtaş
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Contributions

Conceptualization and design: Hakan Oktay Aydınlı. Data collection: Mervegül Aykanat Atay, Berkan Sarıtaş. Analysis of data and interpretation of results: Ali Ekincek, Hakan Oktay Aydınlı. Writing the first draft of the manuscript: Hakan Oktay Aydınlı. Review and editing: Hakan Oktay Aydınlı, Mehtap Özenen-Kavlak.

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Correspondence to Hakan Oktay Aydınlı.

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Aydınlı, H.O., Ekincek, A., Aykanat-Atay, M. et al. Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach. Appl Geomat 14, 669–678 (2022). https://doi.org/10.1007/s12518-022-00462-y

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  • DOI: https://doi.org/10.1007/s12518-022-00462-y

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