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Sinop'un Karadeniz kıyısı için deniz seviyesi tahmininde YSA ve SVR tabanlı modellerin karşılaştırılması

Year 2024, Volume: 10 Issue: 1, 49 - 56, 18.03.2024
https://doi.org/10.52998/trjmms.1342164

Abstract

Deniz suyu seviyesi salınımları, kıyı inşaatı, taşkın önleme ve insan yaşam koşulları için çok büyük önem arz etmektedir. Ancak rüzgar, yağış ve diğer atmosferik koşulların etkileri nedeniyle deniz suyu seviyesinin günlük hareketini doğru bir şekilde tahmin etmek zordur. Bu nedenle bu çalışma bünyesinde Sinop Sahili'nde deniz suyu seviyesi tahmini için yapay zeka (AI) tabanlı Yapay Sinir Ağları (YSA) ve Destek Vektör Regresyon (DVR) yöntemleri uygulanmaktadır. Bunların yanında kıyaslama modeli olarak Çoklu Doğrusal Regresyon (ÇDR) kullanılmaktadır. Model değerlendirme kriteri olarak ise determinasyon katsayısı (R2) ve ortalama karesel hata (RMSE) yöntemleri kullanılmıştır. Bunlarla beraber, Sinop İstasyonu'nun 15 dakikalık (toplamda yaklaşık 22 aylık) deniz suyu seviyesi verileri toplanmış ve olduğu gibi kullanılmıştır. Sonuç olarak bulgular, YSA modelinin sırasıyla 0.84, 0.67, 0.64, 0.63 korelasyon katsayıları (R2) değerleri ile 1., 2., 3. ve 4. günler için su seviyesini tahmin edebildiğini ve DVR modelinin 1., 2. günler için sırasıyla 0.86, 0.66 korelasyon katsayıları (R2) değerleri ile tahmin edebildiğini ortaya koymuştur.

References

  • Alshouny, A., Elnabwy, M.T., Kaloop, M.R., Baik, A., Miky, Y. (2022). An integrated framework for improving sea level variation prediction based on the integration Wavelet-Artificial Intelligence approaches. Environmental Modelling & Software 152: 105399.
  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000). Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering 5(2): 115-123.
  • Balogun, A.L., Adebisi, N. (2021). Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy. Geomatics, Natural Hazards and Risk 12(1): 653-674.
  • Bernstein, A., Gustafson, M.T., Lewis, R. (2019). Disaster on the horizon: The price effect of sea level rise. J. Financ. Econ. 134: 253–272. doi: 10.1016/j.jfineco.2019.03.013.
  • Beuzen, T., Splinter, K. (2020). Machine learning and coastal processes. In: “Sandy beach morphodynamics”, pp. 689-710.
  • Chau, K.W., Cheng, C.T. 2002. Real-time prediction of water stage with artificial neural network approach. In Australian Joint Conference on Artificial Intelligence, (pp. 715-715). Springer, Berlin, Heidelberg. doi: 10.1007/3-540-36187-1_64.
  • Guillou, N., Chapalain, G. (2021). Machine learning methods applied to sea level predictions in the upper part of a tidal estuary. Oceanologia 63(4): 531-544.
  • Imani, M., Kao, H.C., Lan, W.H., Kuo, C.Y. (2017). Daily sea level prediction at chiayi coast, taiwan using extreme learning machine and relevance vector machine. Global Planet. Change 161. doi: 10.1016/j.gloplacha.2017.12.018, 211-211.
  • Jin, H., Zhong, R., Liu, M., Ye, C., Chen, X. (2023). Using EEMD mode decomposition in combination with machine learning models to improve the accuracy of monthly sea level predictions in the coastal area of China. Dynamics of Atmospheres and Oceans 102: 101370.
  • Karsavran, Y., Erdik, T., Terzioğlu, Z.Ö. (2020). The Effect of the Peak Discharges of River Danube on Istanbul Strait (Bosphorus). International Journal of Environment and Geoinformatics 7(2): 108-113.
  • Karsavran, Y., Erdik, T. (2021). Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait. International Journal of Mathematical. Engineering and Management Sciences 6(5): 1242.
  • Lin, G.Q., Li, L.L., Tseng, M.L., Liu, H.M., Yuan, D.D., Tan, R.R. (2020). An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. Journal of Cleaner Production 253: 119966. doi: 10.1016/j.jclepro.2020.119966.
  • Meilianda, E., Pradhan, B., Comfort, L.K., Alfian, D., Juanda, R., Syahreza, S., Munadi, K. (2019). Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia. International Journal of Disaster Risk Reduction 41: 101292.
  • Patil, S.G., Mandal, S., Hegde, A.V. (2012). Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multilayer moored floating pipe breakwater. Adv. Eng. Software 45: 203–212. doi: 10.1016/j.advengsoft.2011.09.026.
  • Primo de Siqueira, B.V., Paiva, A. de M. (2021). Using neural network to improve sea level prediction along the southeastern Brazilian coast. Ocean Model 168: 101898. doi: 10.1016/j.ocemod.2021.101898.
  • Röske, F. (1997). Wasserstandsvorhersage mittels neuronaler Netze. Deutsche Hydrografische Zeitschrift 49: 71-99.
  • Song, C., Chen, X., Xia, W., Ding, X., Xu, C. (2022). Application of a novel signal decomposition prediction model in minute sea level prediction. Ocean Engineering 260: 111961.
  • Wang, W.C., Chau, K.W., Cheng, C.T., Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374(3-4): 294-306. doi: 10.1016/j.jhydrol.2009.06.019.
  • Woodworth, P.L., Hunter, J.R., Marcos, M., Hughes, C.W. (2021). Towards reliable global allowances for sea level rise. Glob. Planet. Change 203: 103522. doi: 10.1016/j.gloplacha.2021.103522.
  • Yesudian, A.N., Dawson, R.J. (2021). Global analysis of sea level rise risk to airports. Clim. Risk Manag. 31: 100266. doi: 10.1016/j.crm.2020.100266.
  • 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. Mar. Geodes. 42(4): 1–23. doi: 10.1080/01490419.2019.1626306.
  • Zhao, J., Cai, R., Sun, W. (2021). Regional sea level changes prediction integrated with singular spectrum analysis and long-short-term memory network. Adv. Sp. Res. 68: 4534–4543. doi: 10.1016/j.asr.2021.08.017.

Comparison of ANN and SVR based models in sea level prediction for the Black Sea coast of Sinop

Year 2024, Volume: 10 Issue: 1, 49 - 56, 18.03.2024
https://doi.org/10.52998/trjmms.1342164

Abstract

Seawater level oscillations are very critical to coastal construction, flood prevention and human living conditions. However, it is difficult to accurately project the daily future for seawater level due to the effects of wind, precipitation and other atmospheric conditions. For this reason, in this paper, artificial intelligence (AI) based Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are applied for the estimation of seawater level in Sinop Coast. In addition, Multiple Linear Regression (MLR) is used as a benchmarking model. In this study, coefficient of determination (R2) and root mean square error (RMSE) were applied as model evaluation criteria. Besides, 15 minutes (approximately 22 months) sea water level data of Sinop Station were collected and used as is. The findings revealed that the ANN model can predict the water level for 1st, 2nd, 3rd, 4th days with correlation coefficients (R2) of 0.84, 0.67, 0.64, 0.63, respectively, and the SVR model can predict for 1st, 2nd days with correlation coefficients (R2) of 0.86, 0.66, respectively.

References

  • Alshouny, A., Elnabwy, M.T., Kaloop, M.R., Baik, A., Miky, Y. (2022). An integrated framework for improving sea level variation prediction based on the integration Wavelet-Artificial Intelligence approaches. Environmental Modelling & Software 152: 105399.
  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000). Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering 5(2): 115-123.
  • Balogun, A.L., Adebisi, N. (2021). Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy. Geomatics, Natural Hazards and Risk 12(1): 653-674.
  • Bernstein, A., Gustafson, M.T., Lewis, R. (2019). Disaster on the horizon: The price effect of sea level rise. J. Financ. Econ. 134: 253–272. doi: 10.1016/j.jfineco.2019.03.013.
  • Beuzen, T., Splinter, K. (2020). Machine learning and coastal processes. In: “Sandy beach morphodynamics”, pp. 689-710.
  • Chau, K.W., Cheng, C.T. 2002. Real-time prediction of water stage with artificial neural network approach. In Australian Joint Conference on Artificial Intelligence, (pp. 715-715). Springer, Berlin, Heidelberg. doi: 10.1007/3-540-36187-1_64.
  • Guillou, N., Chapalain, G. (2021). Machine learning methods applied to sea level predictions in the upper part of a tidal estuary. Oceanologia 63(4): 531-544.
  • Imani, M., Kao, H.C., Lan, W.H., Kuo, C.Y. (2017). Daily sea level prediction at chiayi coast, taiwan using extreme learning machine and relevance vector machine. Global Planet. Change 161. doi: 10.1016/j.gloplacha.2017.12.018, 211-211.
  • Jin, H., Zhong, R., Liu, M., Ye, C., Chen, X. (2023). Using EEMD mode decomposition in combination with machine learning models to improve the accuracy of monthly sea level predictions in the coastal area of China. Dynamics of Atmospheres and Oceans 102: 101370.
  • Karsavran, Y., Erdik, T., Terzioğlu, Z.Ö. (2020). The Effect of the Peak Discharges of River Danube on Istanbul Strait (Bosphorus). International Journal of Environment and Geoinformatics 7(2): 108-113.
  • Karsavran, Y., Erdik, T. (2021). Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait. International Journal of Mathematical. Engineering and Management Sciences 6(5): 1242.
  • Lin, G.Q., Li, L.L., Tseng, M.L., Liu, H.M., Yuan, D.D., Tan, R.R. (2020). An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. Journal of Cleaner Production 253: 119966. doi: 10.1016/j.jclepro.2020.119966.
  • Meilianda, E., Pradhan, B., Comfort, L.K., Alfian, D., Juanda, R., Syahreza, S., Munadi, K. (2019). Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia. International Journal of Disaster Risk Reduction 41: 101292.
  • Patil, S.G., Mandal, S., Hegde, A.V. (2012). Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multilayer moored floating pipe breakwater. Adv. Eng. Software 45: 203–212. doi: 10.1016/j.advengsoft.2011.09.026.
  • Primo de Siqueira, B.V., Paiva, A. de M. (2021). Using neural network to improve sea level prediction along the southeastern Brazilian coast. Ocean Model 168: 101898. doi: 10.1016/j.ocemod.2021.101898.
  • Röske, F. (1997). Wasserstandsvorhersage mittels neuronaler Netze. Deutsche Hydrografische Zeitschrift 49: 71-99.
  • Song, C., Chen, X., Xia, W., Ding, X., Xu, C. (2022). Application of a novel signal decomposition prediction model in minute sea level prediction. Ocean Engineering 260: 111961.
  • Wang, W.C., Chau, K.W., Cheng, C.T., Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374(3-4): 294-306. doi: 10.1016/j.jhydrol.2009.06.019.
  • Woodworth, P.L., Hunter, J.R., Marcos, M., Hughes, C.W. (2021). Towards reliable global allowances for sea level rise. Glob. Planet. Change 203: 103522. doi: 10.1016/j.gloplacha.2021.103522.
  • Yesudian, A.N., Dawson, R.J. (2021). Global analysis of sea level rise risk to airports. Clim. Risk Manag. 31: 100266. doi: 10.1016/j.crm.2020.100266.
  • 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. Mar. Geodes. 42(4): 1–23. doi: 10.1080/01490419.2019.1626306.
  • Zhao, J., Cai, R., Sun, W. (2021). Regional sea level changes prediction integrated with singular spectrum analysis and long-short-term memory network. Adv. Sp. Res. 68: 4534–4543. doi: 10.1016/j.asr.2021.08.017.
There are 22 citations in total.

Details

Primary Language English
Subjects Ocean Engineering
Journal Section Research Article
Authors

Yavuz Karsavran 0000-0001-5944-0658

Early Pub Date December 26, 2023
Publication Date March 18, 2024
Submission Date August 12, 2023
Acceptance Date December 19, 2023
Published in Issue Year 2024 Volume: 10 Issue: 1

Cite

APA Karsavran, Y. (2024). Comparison of ANN and SVR based models in sea level prediction for the Black Sea coast of Sinop. Turkish Journal of Maritime and Marine Sciences, 10(1), 49-56. https://doi.org/10.52998/trjmms.1342164

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