Detection of water quality failure events at treatment works using a hybrid two-stage method with CUSUM and random forest algorithms
| Autor: | Zoran Kapelan, Michele Romano, Gerald Riss, Fayyaz Ali Memon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Online monitoring
Computer science 0208 environmental biotechnology CUSUM 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Event recognition Fault detection and isolation River lake and water-supply engineering (General) Water treatment works TD201-500 0105 earth and related environmental sciences Water Science and Technology TC401-506 Water supply for domestic and industrial purposes Event (computing) Statistical process control 020801 environmental engineering Random forest Water quality Stage (hydrology) Data mining F1 score computer |
| Zdroj: | Water Science and Technology: Water Supply, 21(6) Water Supply, Vol 21, Iss 6, Pp 3011-3026 (2021) |
| ISSN: | 1606-9749 |
| Popis: | Near-real-time event detection is crucial for water utilities to be able to detect failure events in their water treatment works (WTW) quickly and efficiently. This paper presents a new method for an automated, near-real-time recognition of failure events at WTWs by the application of combined statistical process control and machine-learning techniques. The resulting novel hybrid CUSUM event recognition system (HC-ERS) uses two distinct detection methodologies: one for fault detection at the level of individual water quality signals and the second for the recognition of faulty processes at the WTW level. HC-ERS was tested and validated on historical failure events at a real-life UK WTW. The new methodology proved to be effective in the detection of failure events, achieving a high true-detection rate of 82% combined with a low false-alarm rate (average 0.3 false alarms per week), reaching a peak F1 score of 84% as a measure of accuracy. The new method also demonstrated higher accuracy compared with the CANARY detection methodology. When applied to real-world data, the HC-ERS method showed the capability to detect faulty processes at WTW automatically and reliably, and hence potential for practical application in the water industry. HIGHLIGHTS The novel HC-ERS combines the conventional SPC-type method with RF advanced machine-learning technique to ultimately detect WTW-level failure events.; When applied on unseen data HC-ERS proved to be capable of detecting failure events in WTW processes in near-real-time.; HC-ERS outperformed threshold-based and CANARY event detection methods.; HC-ERS showed potential for practical application in the water industry. |
| Databáze: | OpenAIRE |
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