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

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...

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Bibliographic Details
Published in:Applied geomatics 2022-12, Vol.14 (4), p.669-678
Main Authors: Aydınlı, Hakan Oktay, Ekincek, Ali, Aykanat-Atay, Mervegül, Sarıtaş, Berkan, Özenen-Kavlak, Mehtap
Format: Article
Language:English
Subjects:
Aquatic environment
Earth and Environmental Science
Geographical Information Systems/Cartography
Geography
Geophysics/Geodesy
Health aspects
Life expectancy
Machine learning
Marine environment
Measurement Science and Instrumentation
Neural networks
Original Paper
Prediction models
Remote sensing
Remote Sensing/Photogrammetry
Satellites
Sea surface
Sea surface temperature
Surface temperature
Surveying
Time series
Weather forecasting
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Summary: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.
ISSN:1866-9298
1866-928X
DOI:10.1007/s12518-022-00462-y