Comparative Analysis of Five Machine Learning Regression Models for Salinity Prediction in the California Current System

Abstract

Salinity significantly influences ocean water movement, climate change, marine ecosystems and heat transfer, impacting processes such as ocean circulation patterns, density-driven currents, sea ice production, nutrient distribution, and the global climate system. Various physics-based equations and oceanographic theories are utilized to measure saltwater salinity in specific locations. This approach fails to account for data fluctuation, harsh environmental conditions, and the detection of oceanographic data patterns. This type of salinity measurement is time-consuming and expensive. This study evaluates five superior regression models: multivariable linear regression (MVR), K nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). After performing five regression models, the XGBoost model demonstrated superior performance compared to the other four models with the highest coefficient of determination (R²). The score recorded was 0.9981, root mean square error (RMSE): 0.0232, mean square error (MSE): 0.0005, and mean absolute error (MAE): 0.0152. Cross- validation was also conducted for the best-performing model for generalization of model output. This study provides a robust and cost-efficient tool and proves the efficiency of the ML model for measuring seawater salinity.