Forecasting Crucial Biogeochemical Indicators of the Southern Ocean for Climate Monitoring Using Modified Kernel-Based Support Vector Regression

The ocean is a massive expanse of saltwater that spans over 70.8% of the Earth’s surface and holds nearly 97% of the planet’s water. The Southern Ocean is remarkable because it influences worldwide climate patterns and plays a key role in storing a tremendous amount of heat, carbon dioxide, and nutrients. As a result, we are focusing our efforts on solving the mysteries surrounding the Southern Ocean. Therefore, in this study, we attempted to forecast crucial biogeochemical indicators for climate monitoring such as pH (25  \(^{\circ }\text {C}\) ), nitrate content, and relative density of seawater. More research and analysis on these three components might be beneficial because they are critical aspects for ecology, marine biochemistry, and overall climate patterns. This study is conducted to develop new custom kernels (Hyperbolic Sine Kernel, Gaussian Matrix Multiplier Kernel) for mapping the features into desirable space, effectively making assumptions based on existing relevant features using SVR technique. Further, comparing their efficiency against present kernels (on certain constraints) across three dimensions and suggesting a significantly higher-performing kernel.