A smart sewer detection approach based on wavelet denoising of in-sewer temperature sensing measurement.

ABSTRACT: Urban sewer detection is important for the proper conveyance of sanitary water to wastewater treatment plant prior to environmental discharge. An effective approach to address this important process still needs to be developed. This study introduced a novel data-driven approach to sewer detection utilizing in-sewer distributed temperature sensing (DTS) measurement combined with wavelet-based denoising of DTS data. It underlines that the effective denoising of DTS data, and consequently the accurate determination of DTS noise threshold, is pivotal to reliable sewer detection. DTS background noise is chiefly influenced by the threshold rescaling. A reliable DTS background noise threshold was found to be ±0.25 °C in a field study, established with the threshold rescaling of a level-dependent

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10719568 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature