Evaluation of the performance of SVR and ANN Models in Sea Water Level Prediction for the Izmir Coast of the Aegean Sea

Year 2024, Volume: 2 Issue: 1, 1 - 7, 20.03.2024

Yavuz Karsavran

https://doi.org/10.61150/ijonfest.2024020101

Abstract

Forecasting of sea water level is a very important phenomenon for making future projections for flood control, human living conditions and coastal planning. However, it is not easy to estimate sea water level due to the influence of atmospheric conditions. Thus, Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are used for the prediction of seawater level of Izmir Coast based on the time series data. Coefficient of determination (R2) and root mean square error (RMSE) are applied as model evaluation criteria in this study. 19 months of seawater level data in the time series were used in this study. The results show that the ANN model can predict the water level with R2 of 0.84 and 0.68 for 1st and 2nd days, respectively, while the SVR model can predict the water level with R2 of 0.83 and 0.69 for 1st and 2nd days, respectively.

Keywords

ANN, SVR, Sea Level Prediction, Izmir coast, Aegean Sea

References

• Aksoy, H., Demirel, H., & Seker, D.Z., 2017. Exploring possible impacts of sea level rise: the case of Izmir, Turkey. International Journal of Global Warming, 13(3-4), 398-410.
• Alpar, B., Burak, S., & GAZİOGLU, C., 1997. Effect of weather system on the regime of sea level variations in İzmir Bay. Journal of Black Sea/Mediterranean Environment, 3(2).
• 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, 2000. Artificial neural networks in hydrology; I: Preliminary concepts. J Hydrol Eng 5(2),115–123. https:// doi.org/ 10. 1061/ (ASCE) 1084- 0699(2000)5: 2(115).
• 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.
• 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.
• Çoşkun, E., & Balas, L., 2018. Sea Level Changes in Izmir Bay. Int. J. Eng. Res. Dev., 14(9), 68-73. Erdik, T., Savci, M.E., & Sen, Z., 2009. Artificial neural networks for predicting maximum wave runup on rubble mound structures. Expert Systems with Applications, 36(3, part 2), 6403-6408.
• 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., 2018. Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Global and planetary change 161, 211-221.
• 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., 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.
• Karsavran, Y., 2023. 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, 1-8. doi: 10.52998/trjmms. 1342164.
• Karsavran, Y., Erdik, T., & Ozger, M., 2023. An improved technique for streamflow forecasting between Turkish straits. Acta Geophysica,, 1-12. https://doi.org/10.1007/s11600-023-01216-z.
• 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.
• 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.
• Röske, F., 1997. Wasserstandsvorhersage mittels neuronaler Netze. Deutsche Hydrografische Zeitschrift 49, 71-99.
• Seo, Y., Kim, S., Kisi, O., & Singh, V. P., 2015. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology 520, 224-243.
• 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.
• Türkseven, O.D., Kısacık, D., Baykal, C., & Güler, I., 2023. Soft Measures Against to Coastal Flooding as A Result of Expected Sea Level Rise in Izmir Bay.
• 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. https://doi.org/10.1016/j.jhydrol.2009.06.019.
• 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.