Influence of Particle Shape on Polarization Characteristics of Backscattering Light in Turbid Media

Ye, Kuntao; Musha, 吉俄木沙 Ji''E; Shengjie, 翟盛杰 Zhai

doi:10.3788/cjl202047.0105004

Cited by 2 publications

(1 citation statement)

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“…The classic method for turbidity measurement uses the Secchi disk [9]. Currently, the predominant methods for turbidity measurement hinge on physical principles, particularly the scattering of light [10,11] and sound [12,13]. Techniques such as nephelometry and turbidimetry quantify turbidity, expressed in nephelometric turbidity units (NTUs), by assessing the intensity of light scattered by suspended particles in water [14].…”

Section: Introductionmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

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Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

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