Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water...

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 Feature Interaction Network (AutoInt) is coupled with the Ninja Optimization Algorithm (NiOA) for wrapper-based hyperparameter optimization under a controlled and reproducible experimental protocol. All evaluations use a fixed train/validation/test split (70%/15%/15%) with leakage-safe preprocessing based only on training-set statistics, and results are aggregated over [Formula: see text] independent runs with controlled initialization seeds and mean ± standard deviation reporting. Baseline AutoInt without metaheuristic optimization achieves a mean squared error of [Formula: see text], whereas the NiOA-optimized AutoInt configuration reaches [Formula: see text] under the matched optimization budget of 1500 function evaluations ([Formula: see text], [Formula: see text]). These findings indicate that NiOA-guided hyperparameter tuning can substantially improve AutoInt performance within this fixed benchmark setting. However, because the dataset is small, cross-sectional, and lacks explicit spatial or temporal structure, the results should be interpreted as benchmark-specific evidence rather than broad operational validation. Further evaluation on larger, independent, and spatiotemporally diverse water-quality datasets is required before generalizing the fitted model to wider environmental monitoring or decision-support applications.