Water Quality Monitoring and Advisory Systems Using Real-Time Sensors and GPTs for Precision Brackishwater Aquaculture

Certifying excellent water quality is mandatory for fish health and productivity in brackish water aquaculture. The old manual testing methods are time-consuming and cannot provide continuous monitoring of water parameters. This article highlights the IoT-based smart water quality monitoring and advisory system that reads real-time sensors with machine learning for predictive analytics. An Arduino-based sensor network persistently monitors the parameters like pH (6.5–8.5), dissolved oxygen (DO: 4–8 mg/L) and oxidation-reduction potential (ORP: 200–400 mV) with cloud archive to perform analysis. A Long Short-Term Memory (LSTM) model, which specifically captures the long-term dependencies in time-series data, is employed to predict water quality trends with 94.2% accuracy, enabling early detection of fluctuations. This authorized model efficiently learns physical devices and creates accurate pH, DO, and ORP predictions. The reliability of the model is ensured by graphical analysis, which encompasses residual distributions, training-validation loss curves, feature correlation heatmaps, and actual vs. anticipated values. Real-time sensor data collaborative with AI-driven predictive perception improves decision-making, reduces the risk of fish death, and increases the sustainability of aquaculture. With R2 values for DO = (0.95), ORP = (0.60), and pH = (0.98), the pH model has a high interpretative power, accounting for nearly all variance in the dataset. In contrast, the ORP model accounts for only 60% of the variance, indicating the need for more feature additions to improve the accuracy of the prediction.