Research Article
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Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini

Year 2021, Volume: 8 Issue: 2, 84 - 103, 01.11.2021
https://doi.org/10.9733/JGG.2021R0007.T

Abstract

Anlık deniz seviyesinin tahmini, jeodezik düşey datumun belirlenmesi ve güncellenmesi, kıyı alanlarının korunması, kıyı ekosistemlerinin izlenmesi, kıyı yapılarının planlanması ve bakımı, iklim değişikliği etkilerinin gözlenmesi açısından büyük önem taşımaktadır. Anlık deniz seviyesi tahmini için kullanılan geleneksel yöntemler genellikle doğrusal varsayımlara dayanmaktadır. Ancak deniz seviyesini etkileyen faktörler çok çeşitlidir ve etkileri bölgeden bölgeye değişmektedir. Genellikle doğrusal olmayan ve karmaşık bağımlılık yapılarına sahiptirler. Bu nedenle, doğrusal olmayan deniz seviyeleri doğrusal modeller kullanılarak yüksek duyarlıkta belirlenemez. Makine öğrenimi tahmin yöntemleri ise, son zamanlarda değişkenler arasındaki karmaşık bağımlılık yapılarının modellenmesinde sıklıkla kullanılmaktadır. Bu çalışma kapsamında, anlık deniz seviyesini yüksek doğrulukta tahmin etmek ve doğrusal tahmin yöntemleri ile doğrusal olmayan tahmin yöntemlerini karşılaştırmak amacıyla makine öğrenimi tahmin yöntemlerinden Çoklu Doğrusal Regresyon (ÇDR) doğrusal modeli, Destek Vektör Regresyonu (DVR) doğrusal olmayan model ve Rastgele Orman Regresyonu (ROR) doğrusal olmayan model algoritmaları kullanılmış ve tahmin performansları karşılaştırılmıştır. Çalışma sonucunda anlık deniz seviyesi için en yüksek tahmin performansı ROR ile elde edilmiş olup, en düşük tahmin performansı ise ÇDR yöntemi ile elde edilmiştir. Sonuç olarak anlık deniz seviyelerinin çalışmada kullanılan öncül bilgiler ile ROR kullanılarak yüksek hassasiyette tahmin edilebileceği ve doğrusal tahmin modelinin anlık deniz seviyesinin karmaşık bağımlılık yapısının modellenmesinde yetersiz olduğu gösterilmiştir.

Thanks

Çalışmada kullanılan TUDES verilerini https://tudes.harita.gov.tr/ linki üzerinden sağlayan Harita Genel Müdürlüğüne teşekkür ederiz.

References

  • Arns, A., Wahl, T., Wolff, C., Vafeidis, A. T., Haigh, I. D., Woodworth, P., Niehüser, S., & Jensen, J. (2020). Non-linear interaction modulates global extreme sea levels, coastal flood exposure, and impacts. Nature communications, 11(1), 1-9.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Brundrit, G. B. (1995). Trends of southern African sea level: statistical analysis and interpretation. South African journal of marine science, 16(1), 9-17.
  • Chanklan, R., Kaoungku, N., Suksut, K., Kerdprasop, K., & Kerdprasop, N. (2018). Runoff Prediction with a combined artificial neural network and support vector regression. International Journal of Machine Learning and Computing, 8(1).
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Costa, C. G., Leite, J. R. B., Castro, B. M., Blumberg, A. F., Georgas, N., Dottori, M., & Jordi, A. (2020). An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil. Ocean Dynamics, 70(2), 257-271.
  • Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in neural information processing systems, 9, 155-161.
  • Ertuğrul, Ö. F., & Tağluk, M. E. (2017). Forecasting local mean sea level by generalized behavioral learning method. Arabian Journal for Science and Engineering, 42(8), 3289-3298.
  • Fu, Y., Zhou, X., Sun, W., & Tang, Q. (2019). Hybrid model combining empirical mode decomposition, singular spectrum analysis, and least squares for satellite-derived sea-level anomaly prediction. International journal of remote sensing, 40(20), 7817-7829.
  • Ghorbani, M. A., Khatibi, R., Aytek, A., Makarynskyy, O., & Shiri, J. (2010). Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Computers & Geosciences, 36(5), 620-627.
  • Gülaçar, H. (2018). Nesnelerin İnterneti Platformları İçin Makine Öğrenmesi Tabanlı Bir Tahmin Modülü. (Yüksek Lisans Tezi), İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Güven, A., & Günal, M. (2008). Genetic programming approach for prediction of local scour downstream of hydraulic structures. Journal of Irrigation and Drainage Engineering, 134(2), 241-249.
  • Huang, M., Peng, G., Zhang, J., & Zhang, S. (2006). Application of artificial neural networks to the prediction of dust storms in Northwest China. Global and Planetary change, 52(1-4), 216-224.
  • Imani, M., You, R. J., & Chung-Yen, K. (2013). Accurate Forecasting of the satellite-derived seasonal Caspian sea level anomaly using polynomial interpolation and holt-winters exponential smoothing. Tao: terrestrial, atmospheric and oceanic sciences, 24(4), 521.
  • Imani, M., You, R. J., & Kuo, C. Y. (2014a). Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming. Global and planetary change, 121, 53-63.
  • Imani, M., You, R. J., & Kuo, C. Y. (2014b). Caspian Sea level prediction using satellite altimetry by artificial neural networks. International journal of environmental science and technology, 11(4), 1035-1042.
  • Imani, M., Kao, H. C., Lan, W. H., & Kuo, C. Y. (2018). Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Global and planetary change, 161, 211-221.
  • Irvine, K. N., & Eberhardt, A. J. (1992). Multiplicative, Seasonal Arima Models for Lake Erie And Lake Ontario Water Levels 1. JAWRA Journal of the American Water Resources Association, 28(2), 385-396.
  • Juva, K., Flögel, S., Karstensen, J., Linke, P., & Dullo, W. C. (2020). Tidal dynamics control on cold-water coral growth: A high-resolution multivariable study on eastern Atlantic cold-water coral sites. Frontiers in Marine Science, 7(132).
  • Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
  • Kisi, O., Shiri, J., & Nikoofar, B. (2012). Forecasting daily lake levels using artificial intelligence approaches. Computers & Geosciences, 41, 169-180.
  • Makarynskyy, O., Makarynska, D., Kuhn, M., & Featherstone, W. E. (2004). Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science, 61(2), 351-360.
  • Meshkani, M. R., & Meshkani, A. (1997). Stochastic modelling of the Caspian Sea level fluctuations. Theoretical and applied climatology, 58(3), 189-195.
  • More, A., & Deo, M. C. (2003). Forecasting wind with neural networks. Marine structures, 16(1), 35-49.
  • Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. International Conference on Artificial Neural Networks (s. 999-1004). Berlin, Heidelberg: Springer.
  • Pawlowicz, R., Beardsley, B., & Lentz, S. (2002). Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Computers & Geosciences, 28(8), 929-937.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
  • Pugh, D. T. (1996). Tides, surges and mean sea-level (reprinted with corrections). Chichester, UK: John Wiley & Sons Ltd.
  • Rajasekaran, S., Gayathri, S., & Lee, T. L. (2008). Support vector regression methodology for storm surge predictions. Ocean Engineering, 35(16), 1578-1587.
  • Roshni, T., Samui, P., & Drisya, J. (2019). Operational use of machine learning models for sea-level modeling. Indian Journal of Geo Marine Sciences, 48(9), 1427-1434.
  • Sezen, E. (2006). Antalya-I (1935-1977) ve Antalya-II (1985-2005) Mareograf İstasyonlarında Deniz Seviyesi Değişimlerinin Araştırılması. (Yüksek Lisans Tezi), Afyon Kocatepe Üniversitesi, Afyonkarahisar, Türkiye.
  • Srivastava, P. K., Islam, T., Singh, S. K., Petropoulos, G. P., Gupta, M., & Dai, Q. (2016). Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data. Meteorological applications, 23(4), 633-639.
  • Şen, Z., Kadıoğlu, M., & Batur, E. (2000). Stochastic modeling of the Van Lake monthly level fluctuations in Turkey. Theoretical and applied climatology, 65(1), 99-110.
  • Talebizadeh, M., & Moridnejad, A. (2011). Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Systems with applications, 38(4), 4126-4135.
  • Teferle, F. N. (2003). Strategies for Long Term Monitoring of Tide Gauges Using GPS. (Doktora Tezi). University of Nottingham, Nottingham, İngiltere.
  • Vaziri, M. (1997). Predicting Caspian Sea surface water level by ANN and ARIMA models. Journal of waterway, port, coastal, and ocean engineering, 123(4), 158-162.
  • Wu, C. L., & Chau, K. W. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23(8), 1350-1367.
  • Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
  • Yüksel, Y., & Esin, Ç. (2016). Kıyı Mühendisliği. İstanbul, Türkiye:.BETA Yayınevi.
  • Yüksel, Y., Öztürk, M., Şahin, C., Halat, O., Doğan, U., Yüksel, Z. T., & Karova, C. (2018). Türkiye Denizlerinde Su Seviyesi Değişimi. 9. Kıyı Mühendisliği Sempozyumu, Adana, Türkiye.
  • Zhao, J., Fan, Y., & Mu, Y. (2019). Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach. Marine geodesy, 42(4), 344-366.
  • URL-1: https://tudes.harita.gov.tr/, (Erişim Tarihi : 06 Haziran 2019).

Forecasting instantaneous sea level changes with machine learning methods in Aegean Sea

Year 2021, Volume: 8 Issue: 2, 84 - 103, 01.11.2021
https://doi.org/10.9733/JGG.2021R0007.T

Abstract

Forecasting instantaneous sea-level is of great importance in terms of determination of geodetic vertical datum and updating, conservation of coastal areas, monitoring coastal ecosystems, maintenance, and planning of coastal structures, monitoring of climate change effects. Traditional methods used for instantaneous sea level estimation are often based on linear assumptions. However, contributors to sea levels are very various and their effects vary from region to region. Generally, they have complex and nonlinear dependence structures. Therefore, nonlinear sea-level cannot be determined with high precision using linear models. Recently, machine learning prediction methods have been frequently used in the modelling of complex dependency structures between variables. Within the scope of this study, to predict the instantaneous sea level with high accuracy and to compare linear estimation methods with nonlinear estimation methods, the Multiple Linear Regression (MLR) linear model, Support Vector Regression (SVR) non-linear model, and Random Forest Regression (RFR) non-linear model algorithms were used, and their prediction performances were compared. As a result of the study, the highest prediction performance for instantaneous sea level was obtained with RFR, and the lowest prediction performance was obtained with the MLR method. As a result, it has been shown that instantaneous sea level can be predicted with high precision using RFR with the features used in this study, and the linear prediction models are insufficient in modelling the complex dependency structure of instantaneous sea level.  

References

  • Arns, A., Wahl, T., Wolff, C., Vafeidis, A. T., Haigh, I. D., Woodworth, P., Niehüser, S., & Jensen, J. (2020). Non-linear interaction modulates global extreme sea levels, coastal flood exposure, and impacts. Nature communications, 11(1), 1-9.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Brundrit, G. B. (1995). Trends of southern African sea level: statistical analysis and interpretation. South African journal of marine science, 16(1), 9-17.
  • Chanklan, R., Kaoungku, N., Suksut, K., Kerdprasop, K., & Kerdprasop, N. (2018). Runoff Prediction with a combined artificial neural network and support vector regression. International Journal of Machine Learning and Computing, 8(1).
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Costa, C. G., Leite, J. R. B., Castro, B. M., Blumberg, A. F., Georgas, N., Dottori, M., & Jordi, A. (2020). An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil. Ocean Dynamics, 70(2), 257-271.
  • Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in neural information processing systems, 9, 155-161.
  • Ertuğrul, Ö. F., & Tağluk, M. E. (2017). Forecasting local mean sea level by generalized behavioral learning method. Arabian Journal for Science and Engineering, 42(8), 3289-3298.
  • Fu, Y., Zhou, X., Sun, W., & Tang, Q. (2019). Hybrid model combining empirical mode decomposition, singular spectrum analysis, and least squares for satellite-derived sea-level anomaly prediction. International journal of remote sensing, 40(20), 7817-7829.
  • Ghorbani, M. A., Khatibi, R., Aytek, A., Makarynskyy, O., & Shiri, J. (2010). Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Computers & Geosciences, 36(5), 620-627.
  • Gülaçar, H. (2018). Nesnelerin İnterneti Platformları İçin Makine Öğrenmesi Tabanlı Bir Tahmin Modülü. (Yüksek Lisans Tezi), İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Güven, A., & Günal, M. (2008). Genetic programming approach for prediction of local scour downstream of hydraulic structures. Journal of Irrigation and Drainage Engineering, 134(2), 241-249.
  • Huang, M., Peng, G., Zhang, J., & Zhang, S. (2006). Application of artificial neural networks to the prediction of dust storms in Northwest China. Global and Planetary change, 52(1-4), 216-224.
  • Imani, M., You, R. J., & Chung-Yen, K. (2013). Accurate Forecasting of the satellite-derived seasonal Caspian sea level anomaly using polynomial interpolation and holt-winters exponential smoothing. Tao: terrestrial, atmospheric and oceanic sciences, 24(4), 521.
  • Imani, M., You, R. J., & Kuo, C. Y. (2014a). Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming. Global and planetary change, 121, 53-63.
  • Imani, M., You, R. J., & Kuo, C. Y. (2014b). Caspian Sea level prediction using satellite altimetry by artificial neural networks. International journal of environmental science and technology, 11(4), 1035-1042.
  • Imani, M., Kao, H. C., Lan, W. H., & Kuo, C. Y. (2018). Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Global and planetary change, 161, 211-221.
  • Irvine, K. N., & Eberhardt, A. J. (1992). Multiplicative, Seasonal Arima Models for Lake Erie And Lake Ontario Water Levels 1. JAWRA Journal of the American Water Resources Association, 28(2), 385-396.
  • Juva, K., Flögel, S., Karstensen, J., Linke, P., & Dullo, W. C. (2020). Tidal dynamics control on cold-water coral growth: A high-resolution multivariable study on eastern Atlantic cold-water coral sites. Frontiers in Marine Science, 7(132).
  • Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
  • Kisi, O., Shiri, J., & Nikoofar, B. (2012). Forecasting daily lake levels using artificial intelligence approaches. Computers & Geosciences, 41, 169-180.
  • Makarynskyy, O., Makarynska, D., Kuhn, M., & Featherstone, W. E. (2004). Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science, 61(2), 351-360.
  • Meshkani, M. R., & Meshkani, A. (1997). Stochastic modelling of the Caspian Sea level fluctuations. Theoretical and applied climatology, 58(3), 189-195.
  • More, A., & Deo, M. C. (2003). Forecasting wind with neural networks. Marine structures, 16(1), 35-49.
  • Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. International Conference on Artificial Neural Networks (s. 999-1004). Berlin, Heidelberg: Springer.
  • Pawlowicz, R., Beardsley, B., & Lentz, S. (2002). Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Computers & Geosciences, 28(8), 929-937.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
  • Pugh, D. T. (1996). Tides, surges and mean sea-level (reprinted with corrections). Chichester, UK: John Wiley & Sons Ltd.
  • Rajasekaran, S., Gayathri, S., & Lee, T. L. (2008). Support vector regression methodology for storm surge predictions. Ocean Engineering, 35(16), 1578-1587.
  • Roshni, T., Samui, P., & Drisya, J. (2019). Operational use of machine learning models for sea-level modeling. Indian Journal of Geo Marine Sciences, 48(9), 1427-1434.
  • Sezen, E. (2006). Antalya-I (1935-1977) ve Antalya-II (1985-2005) Mareograf İstasyonlarında Deniz Seviyesi Değişimlerinin Araştırılması. (Yüksek Lisans Tezi), Afyon Kocatepe Üniversitesi, Afyonkarahisar, Türkiye.
  • Srivastava, P. K., Islam, T., Singh, S. K., Petropoulos, G. P., Gupta, M., & Dai, Q. (2016). Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data. Meteorological applications, 23(4), 633-639.
  • Şen, Z., Kadıoğlu, M., & Batur, E. (2000). Stochastic modeling of the Van Lake monthly level fluctuations in Turkey. Theoretical and applied climatology, 65(1), 99-110.
  • Talebizadeh, M., & Moridnejad, A. (2011). Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Systems with applications, 38(4), 4126-4135.
  • Teferle, F. N. (2003). Strategies for Long Term Monitoring of Tide Gauges Using GPS. (Doktora Tezi). University of Nottingham, Nottingham, İngiltere.
  • Vaziri, M. (1997). Predicting Caspian Sea surface water level by ANN and ARIMA models. Journal of waterway, port, coastal, and ocean engineering, 123(4), 158-162.
  • Wu, C. L., & Chau, K. W. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23(8), 1350-1367.
  • Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
  • Yüksel, Y., & Esin, Ç. (2016). Kıyı Mühendisliği. İstanbul, Türkiye:.BETA Yayınevi.
  • Yüksel, Y., Öztürk, M., Şahin, C., Halat, O., Doğan, U., Yüksel, Z. T., & Karova, C. (2018). Türkiye Denizlerinde Su Seviyesi Değişimi. 9. Kıyı Mühendisliği Sempozyumu, Adana, Türkiye.
  • Zhao, J., Fan, Y., & Mu, Y. (2019). Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach. Marine geodesy, 42(4), 344-366.
  • URL-1: https://tudes.harita.gov.tr/, (Erişim Tarihi : 06 Haziran 2019).

Details

Primary Language Turkish
Subjects Geological Sciences and Engineering (Other)
Journal Section Research Article
Authors

Ahmet YAVUZDOĞAN 0000-0002-9898-4946

Emine TANIR KAYIKÇI 0000-0001-8259-5543

Publication Date November 1, 2021
Submission Date July 4, 2020
Published in Issue Year 2021 Volume: 8 Issue: 2

Cite

APA YAVUZDOĞAN, A., & TANIR KAYIKÇI, E. (2021). Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini. Jeodezi Ve Jeoinformasyon Dergisi, 8(2), 84-103. https://doi.org/10.9733/JGG.2021R0007.T
AMA YAVUZDOĞAN A, TANIR KAYIKÇI E. Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini. hkmojjd. November 2021;8(2):84-103. doi:10.9733/JGG.2021R0007.T
Chicago YAVUZDOĞAN, Ahmet, and Emine TANIR KAYIKÇI. “Ege Denizi’nde Makine öğrenimi yöntemleri Ile anlık Deniz Seviyesi değişimlerinin Tahmini”. Jeodezi Ve Jeoinformasyon Dergisi 8, no. 2 (November 2021): 84-103. https://doi.org/10.9733/JGG.2021R0007.T.
EndNote YAVUZDOĞAN A, TANIR KAYIKÇI E (November 1, 2021) Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini. Jeodezi ve Jeoinformasyon Dergisi 8 2 84–103.
IEEE A. YAVUZDOĞAN and E. TANIR KAYIKÇI, “Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini”, hkmojjd, vol. 8, no. 2, pp. 84–103, 2021, doi: 10.9733/JGG.2021R0007.T.
ISNAD YAVUZDOĞAN, Ahmet - TANIR KAYIKÇI, Emine. “Ege Denizi’nde Makine öğrenimi yöntemleri Ile anlık Deniz Seviyesi değişimlerinin Tahmini”. Jeodezi ve Jeoinformasyon Dergisi 8/2 (November 2021), 84-103. https://doi.org/10.9733/JGG.2021R0007.T.
JAMA YAVUZDOĞAN A, TANIR KAYIKÇI E. Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini. hkmojjd. 2021;8:84–103.
MLA YAVUZDOĞAN, Ahmet and Emine TANIR KAYIKÇI. “Ege Denizi’nde Makine öğrenimi yöntemleri Ile anlık Deniz Seviyesi değişimlerinin Tahmini”. Jeodezi Ve Jeoinformasyon Dergisi, vol. 8, no. 2, 2021, pp. 84-103, doi:10.9733/JGG.2021R0007.T.
Vancouver YAVUZDOĞAN A, TANIR KAYIKÇI E. Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini. hkmojjd. 2021;8(2):84-103.