Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches
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RESEARCH ARTICLE - HYDROLOGY
Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches S. Sithara1 · S. K. Pramada1 · Santosh G. Thampi1 Received: 1 June 2020 / Accepted: 8 September 2020 © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2020
Abstract Climate change is expected to adversely affect 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 efficiently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identification of climatic variables influencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the influencing 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 influencing sea level. Halosteric effect is found to have a major impact on the sea level. The variables identified 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 coefficient (r), coefficient of determination (r2), average squared error, Nash–Sutcliffe efficiency, 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. Keywords Climate change · Modelling · Sea level · SVM · WSVM
Introduction Accumulation of greenhouse gases in the atmosphere is the main cause of global warming and consequent change in the earth’s climate. Climate change is projected to have serious ramifications on the oceans, sea level rise (SLR) being one of the major impacts. The sea level is changing continuously owing to thermal expansion of seawater, changes in salinity, and melting of glaciers and ice sheets. There are many climatic and other variables contributing to the observed * Santosh G. Thampi [email protected] S. Sithara [email protected] S. K. Pramada [email protected] 1
Department of Civil Engineering, National Institute of Technology Calicut, Kozhikode, Kerala 673601, India
changes in sea level. Some of the short-lived anthropogenic greenhouse gases such as methane (CH4), chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), and hydrofluorocarbons (HFC
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