@article{article_1590677, title={Predicting dissolved oxygen levels in aquatic ecosystems using machine learning models}, journal={Bozok Journal of Engineering and Architecture}, volume={4}, pages={56–65}, year={2025}, author={Arısoy, Çağrı and Süzgen, Enes Eren and Yildiz, Gülbahar}, keywords={Machine Learning Algorithms, Aquatic Ecosystems, Brisbane River, Water Quality}, abstract={This research investigates the performance of several machine learning algorithms in forecasting dissolved oxygen (DO) levels in the Brisbane River, utilizing physicochemical parameters alongside water flow data. We examined algorithms such as Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, XGBoost, and K-Nearest Neighbors, employing evaluation metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE). Among the models, ensemble techniques, particularly Random Forest and XGBoost, exhibited enhanced predictive accuracy and robustness in identifying intricate, non-linear relationships. Analysis revealed that key variables, including pH, salinity, and specific conductance, were significant predictors, a finding corroborated by the correlation matrix. This study underscores the promise of machine learning, particularly ensemble approaches, in improving water quality monitoring and management, providing valuable insights for ecological sustainability and informed policymaking.}, number={1}, publisher={Yozgat Bozok University}