Cross-System Short-Term Dissolved Oxygen Prediction in Aquaponic Systems Using Multivariate Neural Network Models

Aquaponic systems show complex multivariate dynamics in water quality parameters, with dissolved oxygen (DO) being a key indicator of biological stability. This study presents a dynamic multivariate predictive framework for short-term dissolved oxygen forecasting utilizing IoT data gathered from various heterogeneous aquaponic ponds. The issue is redefined as a regression task to forecast future DO values within a brief time-frame (~5 min), enabling early warning functionalities instead of utilizing a rule-based classification method. To ensure structural robustness across systems, we applied intra-pond percentile trimming and normalization procedures to mitigate the differences in scale between ponds. Using a Leave-One-Pond-Out (LOPO) validation scheme, we tested model performance and cross-system generalization. An MLP feedforward neural network with lagged temporal variables had an average RMSE of 0.83 on a normalized scale. Regime-based error analysis showed that the RMSE increased from 0.80 on stable conditions to 1.43 under high-volatile regimes. A comparative LSTM model did not produce substantial performance enhancements. Sensitivity analysis revealed lagged impacts of pH and turbidity on subsequent DO dynamics, indicating the need for operational measures such as aeration modification and suspended solids management.