Impact on Ocean Acidification Along the Hawaii Coastline Using Learning Algorithm

The paper presents a regression model to estimate the impact of carbon dioxide (CO2) on ocean acidification along the Hawaii coastline, resulting from the mixing of CO2 with ocean water. Identifying parameters with significant correlations to ocean acidification enhances the model's accuracy. The study utilizes Linear and Random forest regressor models, comparing their performance based on Mean Square Error (MSE), R2 error, and Accuracy. Remarkably, Linear Regression exhibits superior performance, indicating that CO2 significantly influences potential of hydrogen (pH) levels, thus suggesting the corrosion of corals near the Hawaii Island coastline. By analyzing a standard real-time dataset, this research sheds light on the urgent need to address ocean acidification to protect the vulnerable marine ecosystems in the region.