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Article

Sea Level Prediction Using Machine Learning

1
Department of Civil Engineering, Akdeniz University, Antalya 07070, Turkey
2
Water Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland
3
Department of Civil Engineering, Antalya Bilim University, Antalya 07190, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Zheng Duan
Water 2021, 13(24), 3566; https://doi.org/10.3390/w13243566
Received: 2 November 2021 / Revised: 2 December 2021 / Accepted: 9 December 2021 / Published: 13 December 2021
Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations. View Full-Text
Keywords: sea level; prediction; Antalya; meteorological factor; regression; ANFIS sea level; prediction; Antalya; meteorological factor; regression; ANFIS
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MDPI and ACS Style

Tur, R.; Tas, E.; Haghighi, A.T.; Mehr, A.D. Sea Level Prediction Using Machine Learning. Water 2021, 13, 3566. https://doi.org/10.3390/w13243566

AMA Style

Tur R, Tas E, Haghighi AT, Mehr AD. Sea Level Prediction Using Machine Learning. Water. 2021; 13(24):3566. https://doi.org/10.3390/w13243566

Chicago/Turabian Style

Tur, Rifat, Erkin Tas, Ali T. Haghighi, and Ali D. Mehr. 2021. "Sea Level Prediction Using Machine Learning" Water 13, no. 24: 3566. https://doi.org/10.3390/w13243566

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