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.

预计气候变化将在许多方面对沿海生态系统产生不利影响。沿海地区气候变化的主要后果之一是海平面上升。为了有效地解决这个问题，获得合理准确的未来海平面估计至关重要。这项研究主要侧重于确定影响海平面和海平面预测的气候变量。相关分析和小波相干图用于识别影响变量，支持向量机（SVM）和混合小波支持向量机（WSVM）技术用于海平面预测。观察到海面温度，海面盐度和平均海平面压力是影响海平面的主要局部气候变量。人们发现，对海平面的影响主要是起晕作用。确定的变量随后在SVM和WSVM中用作预测变量。WSVM使用离散小波变换对变量进行分解，然后将其输入到SVM模型。使用统计指标（例如均方根误差（RMSE），相关系数（r），确定系数（r ²），平均平方误差，纳什-萨特克利夫效率和百分比偏差以及图形指示符，例如泰勒图和回归误差特征曲线。结果表明，WSVM模型预测的海平面在训练期间的RMSE为0.029 m，在测试阶段的RMSE为0.040 m。SVM的相应值分别为0.043 m和0.069 m。另外，其他统计指标和图形指标也表明，WSVM技术在海平面预测方面优于SVM方法。