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

Yuxi Yang, Li Fu, Qingjun Wei, Yuanfa Feng, Ling Zhu, Yan Dai, Wu Xiao, Ting Fan and Xiu Jin

PLOS ONE, 2025, vol. 20, issue 12, 1-39

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0339347 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 39347&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0339347

DOI: 10.1371/journal.pone.0339347

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().