@article{article_1743888, title={Implementation of a Machine Learning-Based Predictive Model for Assessing pH Variability in Coastal Marine Waters}, journal={MEMBA Su Bilimleri Dergisi}, volume={11}, pages={335–345}, year={2025}, DOI={10.58626/memba.1743888}, author={Işık, Hakan and Karahanlı, Ertan and Palabıyık, Selda}, keywords={Makine öğrenmesi algoritmaları, Yapay zeka tabanlı modelleme, Karadeniz, İklim değişikliği etkileri, Asidifikasyon}, abstract={The increase in temperature, along with the rise in carbon dioxide (CO₂) concentration in the marine environment, poses a challenge for the marine environment. The decrease in pH (acidification) in oceans and seas has direct effects such as species loss and shifts in dominant species, as well as indirect effects such as the reorganisation of ecological functions and changes in community organisation patterns. This study aims to evaluate the predictive ability of the Gaussian Process Regression (GPR) model, a machine learning method. In this study, sea water parameters (01.06.2022-28.03.2025) from the Black Sea Region (Giresun coast) obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) were used as the data set. Sea water pH was predicted using important parameters such as temperature and partial carbon dioxide surface pressure (spCO₂) in sea water. The findings of this study demonstrate that the Rational Quadratic Kernel GPR, trained with high spatial resolution SST and spCO₂ data provided by Copernicus, offers high accuracy and strong generalisation in pH estimation for the Giresun coast, demonstrating superior performance compared to other kernels and thus establishing a robust scientific foundation for the development of climate-sensitive decision support and early warning systems.}, number={3}, publisher={Kastamonu Üniversitesi}