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CABNas-nir: A near-infrared classification for urban pipe network sludge on the fusion algorithm of NAS framework and active learning.

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Title: CABNas-nir: A near-infrared classification for urban pipe network sludge on the fusion algorithm of NAS framework and active learning.
Authors: Yang, Yuxi, Fu, Li, Wei, Qingjun, Feng, Yuanfa, Zhu, Ling, Dai, Yan, Xiao, Wu, Fan, Ting, Jin, Xiu
Source: PLoS ONE; 12/19/2025, Vol. 20 Issue 12, p1-39, 39p
Subject Terms: NEAR infrared spectroscopy, ACTIVE learning, AIR pollution control, SEWAGE sludge, SEWAGE, ARTIFICIAL neural networks, MACHINE learning, SPECTRUM analysis
Abstract: Pipe network sludge is a complex pollutant aggregate deposited during long-term operation of urban sewage pipelines, and a key target for pollution control in environmental monitoring systems. Accurate source classification is critical for treatment optimization, pollution tracing, and resource recovery. Traditional methods have drawbacks like long processing time and low efficiency. Near-infrared spectroscopy (NIR) offers a new approach but faces spectral redundancy, limited samples, and biased features. This paper proposes CABNas-nir, a deep neural network under the neural architecture search (NAS) framework, integrating competitive adaptive reweighted sampling (CARS), baseline drift augmentation, and active learning (AL). It selects key spectral features via CARS to remove redundancy, uses baseline drift to generate augmented samples for small-sample issues, employs AL with K-means to select high-value samples, and constructs an optimal convolutional neural network(CNN)+long short-term memory(LSTM) model via NAS. Experiments show 92.86% accuracy, 14.29% higher than support vector machine (SVM,78.57%) and 35.72% higher than that of extreme gradient boosting (XGBoost,57.14%). SHapley Additive exPlanations (SHAP) analysis shows high-contribution spectra in 1400–1700 nm, with 1600–1700 nm significant. This algorithm significantly enhances the robustness of identifying the sources of pipe network sludge, laying a research foundation for the rapid and accurate identification of pipe network sludge. [ABSTRACT FROM AUTHOR]
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  Data: CABNas-nir: A near-infrared classification for urban pipe network sludge on the fusion algorithm of NAS framework and active learning.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yuxi%22">Yang, Yuxi</searchLink><br /><searchLink fieldCode="AR" term="%22Fu%2C+Li%22">Fu, Li</searchLink><br /><searchLink fieldCode="AR" term="%22Wei%2C+Qingjun%22">Wei, Qingjun</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Yuanfa%22">Feng, Yuanfa</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Ling%22">Zhu, Ling</searchLink><br /><searchLink fieldCode="AR" term="%22Dai%2C+Yan%22">Dai, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Wu%22">Xiao, Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Fan%2C+Ting%22">Fan, Ting</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Xiu%22">Jin, Xiu</searchLink>
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  Data: PLoS ONE; 12/19/2025, Vol. 20 Issue 12, p1-39, 39p
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  Data: <searchLink fieldCode="DE" term="%22NEAR+infrared+spectroscopy%22">NEAR infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22ACTIVE+learning%22">ACTIVE learning</searchLink><br /><searchLink fieldCode="DE" term="%22AIR+pollution+control%22">AIR pollution control</searchLink><br /><searchLink fieldCode="DE" term="%22SEWAGE+sludge%22">SEWAGE sludge</searchLink><br /><searchLink fieldCode="DE" term="%22SEWAGE%22">SEWAGE</searchLink><br /><searchLink fieldCode="DE" term="%22ARTIFICIAL+neural+networks%22">ARTIFICIAL neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22SPECTRUM+analysis%22">SPECTRUM analysis</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Pipe network sludge is a complex pollutant aggregate deposited during long-term operation of urban sewage pipelines, and a key target for pollution control in environmental monitoring systems. Accurate source classification is critical for treatment optimization, pollution tracing, and resource recovery. Traditional methods have drawbacks like long processing time and low efficiency. Near-infrared spectroscopy (NIR) offers a new approach but faces spectral redundancy, limited samples, and biased features. This paper proposes CABNas-nir, a deep neural network under the neural architecture search (NAS) framework, integrating competitive adaptive reweighted sampling (CARS), baseline drift augmentation, and active learning (AL). It selects key spectral features via CARS to remove redundancy, uses baseline drift to generate augmented samples for small-sample issues, employs AL with K-means to select high-value samples, and constructs an optimal convolutional neural network(CNN)+long short-term memory(LSTM) model via NAS. Experiments show 92.86% accuracy, 14.29% higher than support vector machine (SVM,78.57%) and 35.72% higher than that of extreme gradient boosting (XGBoost,57.14%). SHapley Additive exPlanations (SHAP) analysis shows high-contribution spectra in 1400–1700 nm, with 1600–1700 nm significant. This algorithm significantly enhances the robustness of identifying the sources of pipe network sludge, laying a research foundation for the rapid and accurate identification of pipe network sludge. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1371/journal.pone.0339347
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        Text: English
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      – SubjectFull: NEAR infrared spectroscopy
        Type: general
      – SubjectFull: ACTIVE learning
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      – SubjectFull: AIR pollution control
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      – SubjectFull: ARTIFICIAL neural networks
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      – SubjectFull: MACHINE learning
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      – SubjectFull: SPECTRUM analysis
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              Text: 12/19/2025
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