Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction (opens in new tab)

Accurate prediction of water-quality indicators remains challenging in small tabular environmental datasets because physicochemical variables can exhibit nonlinear interdependencies and modern deep-learning models are sensitive to hyperparameter configuration. In this study, the supervised regression task is defined as predicting dissolved oxygen (mg/L) from the remaining measured physicochemical variables using a modest public dataset of 200 samples. To address this task, the Automatic Featu...