TY - JOUR
T1 - Near real-time detection of blockages in the proximity of combined sewer overflows using evolutionary ANNs and statistical process control
AU - Rosin, T. R.
AU - Kapelan, Z.
AU - Keedwell, E.
AU - Romano, M.
PY - 2022
Y1 - 2022
N2 - Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant cleanup costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms.
AB - Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant cleanup costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms.
KW - blockage detection
KW - combined sewer overflow
KW - evolutionary artificial neural network
KW - radar rainfall nowcasts
KW - statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85128456186&partnerID=8YFLogxK
U2 - 10.2166/hydro.2022.036
DO - 10.2166/hydro.2022.036
M3 - Article
AN - SCOPUS:85128456186
SN - 1464-7141
VL - 24
SP - 259
EP - 273
JO - Journal of Hydroinformatics
JF - Journal of Hydroinformatics
IS - 2
ER -