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Updated: Feb 12, 2026

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Predicting water quality parameters using proximal spectral sensing technology and adaptive ensemble regression.

Yubo Zhao1, Jie Zhan1, Jinling Chen1

  • 1Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; University of Chinese Academy of Sciences, Beijing 100049, China.

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Summary

This study introduces a novel spectral sensing system for continuous, non-contact water quality monitoring, accurately measuring turbidity, chlorophyll-a, chemical oxygen demand, and dissolved oxygen with high predictive accuracy.

Keywords:
Adaptive ensemble regression frameworkFeature engineeringProximal spectral sensing systemWater quality monitoring

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Conventional and remote-sensing water quality monitoring methods have limitations.
  • There is a need for continuous, non-contact surface water quality assessment.

Purpose of the Study:

  • To develop a proximal spectral sensing system for real-time water quality retrieval.
  • To create an adaptive ensemble regression framework for accurate parameter estimation.

Main Methods:

  • Utilized a 12-band configurable spectral sensor.
  • Implemented an abnormal spectral curve removal framework, feature augmentation, and RFECV.
  • Employed an adaptive ensemble regression framework with base learners (RFR, GBR, GPR, KNNR) and ensemble strategies (weighted averaging, stacking).

Main Results:

  • Achieved high R2 values for turbidity (0.988), chlorophyll-a (0.814), COD (0.882), and DO (0.833).
  • Demonstrated low Mean Absolute Percentage Errors (MAPE) below 10% for most parameters.
  • Showcased model robustness against noise and stability with limited data, effectively tracking water quality trends.

Conclusions:

  • The developed proximal spectral sensing system offers a stable, low-cost solution for high-frequency, continuous surface water quality monitoring.
  • Ensemble strategies are advantageous for non-optically active parameters, while tree-based models excel for optically active ones.