The University of Sheffield
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CCWI2017: F54 'Data-driven Approach to Short-Term Forecasting of Turbidity in a Trunk Main Network'

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journal contribution
posted on 2017-09-01, 15:02 authored by Gregory Meyers, Zoran Kapelan, Edward Keedwell
Water discolouration is an increasingly important and expensive issue due stricter regulatory demands and ageing Water Distribution Systems (WDSs) in the UK and abroad. This paper presents a turbidity forecasting methodology capable of aiding operational staff and enabling proactive management strategies. The methodology presented here does not require a hydraulic or water quality network model that can be expensive to build and maintain. The methodology is tested and verified on a real UK trunk main network with observed turbidity measurement data. Results obtained show that the classification based forecasts of turbidity can reliably detect if discolouration material is mobilised up to 5 hours ahead. The methodology could be used as an early warning system to enable a range of proactive management strategies as an alternative to regular trunk mains cleaning.