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This study is related to distribution temperature sensing (DTS) in sewers for tracing illicit or unintended inflows to foul sewers. A DTS measurement is performed with a fiber optic cable that is installed at the invert of a sewer pipe in combination with a standalone laser/computer instrument. This set-up generates in-sewer temperature measurements with high resolutions in time (every minute) and space (every metre) along the cable over long periods of time (weeks on end). The prolonged monitoring period in combination with the high level of detail in the dataset allows the study of anomalies (i.e., unexpected temperatures and/or temperature variations at certain locations), even if these only occur very infrequently. The objective of this paper is to develop an automated tool to analyze the large data masses and identify anomalies caused by illicit or unintended inflows. In this study, an algorithm for detecting the temperature changes that are caused by both wastewater discharge and inflow of stormwater are developed. A comparison of the results of the automated procedure to the results of a manual assessment of the datasets (Elmehaven, Denmark) shows that the automated procedure performs very well.

Automated monitoring system for events detection in sewer network by distribution temperature sensing data measurement