Multisensor Data Fusion for Real-Time Water Quality Monitoring and Aquaculture Management in Floating Marine Systems
Keywords:
multisensor fusion; aquaculture management; water quality; Bayesian state estimation; sensor reliability; real-time monitoringAbstract
Floating marine aquaculture exposes water-quality sensing to waves, biofouling, variable salinity, and intermittent power and communication, so single-sensor alarms and static fusion rules can produce late or biased decisions. This manuscript develops a reliability-aware hierarchical Bayesian fusion framework for real-time water-quality monitoring and management in floating marine systems. The framework represents temperature, salinity, dissolved oxygen, pH, un-ionized ammonia, and turbidity as a coupled state, combines measurement-level redundancy with model-based state estimation, and allows covariance and credibility weights to evolve with residual statistics, calibration age, and cleaning actions. A residual-based anomaly detector separates sensor faults from genuine hydrochemical events, while a decision layer converts posterior states into feed, aeration, and harvesting actions under explicit loss functions. The central contribution is not a new sensor but an integrated information-processing design that treats reliability as a dynamic state rather than a fixed tuning parameter. Analytical comparison shows why exponential reliability penalties and robust residual tests outperform static averaging when fouling is spatially clustered and when pH-temperature interactions alter ammonia toxicity. The framework provides testable design rules for edge implementation, uncertainty reporting, and maintenance scheduling. It also clarifies where learned models are appropriate as residual and event classifiers rather than as replacements for conservative water-quality state models. The resulting architecture supports defensible aquaculture decisions under communication and energy constraints without requiring a dense sensor grid.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
