Understanding the health of our oceans requires precise, measurable data. The challenge of accurately assessing chemical oxygen demand (COD) in coastal waters, a critical indicator of organic pollution, has long been complicated by environmental factors like turbidity. These natural variations can create spectral interference, obscuring the true signal and hindering the effectiveness of traditional modeling techniques. This study, by presenting a novel integrated approach, directly addresses these limitations, offering a more robust and reliable method for real-time COD monitoring. The innovation lies in the synergistic combination of advanced spectral analysis and intelligent data integration, moving beyond conventional approaches to deliver significantly improved accuracy in complex marine environments.
The proposed methodology, centered on a Principal Component Disturbance Feedback Turbidity Compensation (PC-FDTC Hybrid) and a Stacking-based Variable Selection (SBVS) strategy, represents a significant leap forward. The PC-FDTC Hybrid ingeniously utilizes the first principal component to stabilize spectral baselines, effectively compensating for turbidity-induced distortions. Simultaneously, the SBVS strategy employs a meta-learning approach, integrating multiple sophisticated algorithms to identify the most informative and resilient wavelengths within the UV–Vis spectra. This targeted selection process not only enhances predictive power but also dramatically reduces spectral complexity, by over 96% in this study. Such a reduction is paramount for developing compact, field-deployable sensor systems capable of delivering actionable intelligence.
The results from analyzing 646 seawater samples from Jiaozhou Bay underscore the efficacy of this integrated approach. The SBVS-PLSR model, enhanced by Savitzky–Golay smoothing and the PC-FDTC compensation, achieved an outstanding R² of 0.993 and an RMSEP of 0.340. These metrics represent a substantial improvement over traditional methods, validating the model's superior prediction accuracy and robustness. This development is not merely an incremental improvement; it provides a clear pathway towards more effective ocean stewardship. By enabling precise, real-time COD monitoring even in turbid waters, this technology empowers researchers and policymakers with the validated data necessary to make informed decisions about pollution control and the long-term health of our vital marine ecosystems. The commitment to scientific integrity and technological innovation demonstrated here is fundamental to building a future where ocean intelligence drives protection.