Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 \(\hbox {mg}/\hbox {m}^{3}\) $\text{mg}/{\text{m}}^3$ in mid-level inflow regions (3.6 m water elevation) compared to 86 \(\hbox {mg}/\hbox {m}^{3}\) $\text{mg}/{\text{m}}^3$ in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model’s accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.