Sea level prediction using machine learning
|Author:||Tur, Rifat1; Tas, Erkin1; Torabi Haghighi, Ali2;|
1Department of Civil Engineering, Akdeniz University, Antalya 07070, Turkey
2Water Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland
3Department of Civil Engineering, Antalya Bilim University, Antalya 07190, Turkey
|Online Access:||PDF Full Text (PDF, 3.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202201111770
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2022-01-11
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
This research was supported by the Maa- ja vesitekniikan tuki r.y. (MVTT) with the project number 41878.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).