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1 Acta Geophysica

1 2020

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Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches

Sithara S., Pramada S. K., Thampi Santosh G.

Acta Geophysica

2020

Vol. 68, no. 6

1779--1790

Climate change is expected to adversely afect the coastal ecosystem in many ways. One of the major consequences of climate change in coastal areas is sea level rise. In order to manage this problem efciently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identifcation of climatic variables infuencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the infuencing variables, and support vector machine (SVM) and hybrid wavelet support vector machine (WSVM) techniques were used for sea level prediction. Sea surface temperature, sea surface salinity, and mean sea level pressure were observed to be the major local climatic variables infuencing sea level. Halosteric efect is found to have a major impact on the sea level. The variables identifed were subsequently used as predictors in both SVM and WSVM. WSVM employs discrete wavelet transform to decompose the variables before being input to the SVM model. The performance of both the models was compared using statistical measures such as root mean square error (RMSE), correlation coefcient (r), coefcient of determination (r 2 ), average squared error, Nash–Sutclife efciency, and percentage bias along with graphical indicators such as Taylor diagrams and regression error characteristic curves. Results indicate that the WSVM model predicted sea level with an RMSE of 0.029 m during the training and 0.040 m during the testing phases. The corresponding values for SVM are 0.043 m and 0.069 m, respectively. Also, the other statistical measures and graphical indicators suggest that WSVM technique outperforms the SVM approach in the prediction of sea level.