Smart water quality regulation in sustainable aquaponics using PID control and long-term performance analysis

This study evaluates the reliability and suitability of long-term water quality data collected from a sustainable aquaponics system equipped with a pH, dissolved oxygen, and temperature regulation control strategy based on a PID algorithm. Although PID control was implemented to maintain parameters within optimal biological ranges, natural fluctuations and out-of-range measurements were recorded, particularly in pH. Rather than being considered anomalies, these deviations represent realistic environmental variations that must be captured for comprehensive system analysis. A thorough data validation process was conducted, including descriptive statistics, outlier detection, correlation analysis, principal component analysis (PCA), and temporal stability evaluation. Results confirmed the absence of missing data, the presence of controlled variability in dissolved oxygen and temperature, and meaningful correlations between parameters, with pH showing the highest variability. Autocorrelation and long-term trend analyses indicated stable measurement patterns that reflect real-world aquaponic dynamics. The validated dataset provides a robust foundation for future studies, particularly for the development of artificial intelligence (AI)-based predictive models aimed at early detection of fish distress or mortality.