Sea Level Prediction Using Machine Learning
نویسندگان
چکیده
Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea changes using height meteorological factor observations at tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established explore most feasible input combinations prediction. These use lagged (SC1), both (SC2) as predictive modeling. Cross-correlation analysis was conducted determine optimum combination each scenario. Then, several models developed linear regressions (MLR) adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance evaluated terms root mean squared error (RMSE), absolute (MAE), scatter index (SI), Nash Sutcliffe Efficiency (NSE) indices. results showed that adding factors parameters increases accuracy MLR up 33% short-term predictions. Moreover, contributed more precise understanding ANFIS superior SC1- SC2-based combinations.
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ژورنال
عنوان ژورنال: Water
سال: 2021
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w13243566