A new method for the rapid identification of external water types in rainwater pipeline networks using UV–Vis absorption spectroscopy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2025-01, Vol.324, p.124968, Article 124968
• Main Authors: Chen, Xiaowei, Zhao, Nanjing, Zhu, Wanjiang, Yin, Gaofang, Jia, Renqing, Yang, Ruifang, Ma, Mingjun
• Format: Article
• Language: English
• Subjects: External water types in rainwater; Generative Adversarial Network; Long short-term memory; Multiplicative scatter correction; Rapid identification; UV–Vis absorption spectroscopy; Variational mode decomposition
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2024.124968

[Display omitted] •Rapid and accurate identification of external water in rainwater pipelines.•A novel use of MSC combined with VMD for absorption spectroscopy analysis.•A new method for small samples identification of UV–Vis absorption spectroscopy. Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.