Short-term forecasting of turbidity in trunk main networks

Water Res. 2017 Nov 1:124:67-76. doi: 10.1016/j.watres.2017.07.035. Epub 2017 Jul 17.

Abstract

Water discolouration is an increasingly important and expensive issue due to rising customer expectations, tighter regulatory demands and ageing Water Distribution Systems (WDSs) in the UK and abroad. This paper presents a new turbidity forecasting methodology capable of aiding operational staff and enabling proactive management strategies. The turbidity forecasting methodology developed here is completely data-driven and does not require hydraulic or water quality network model that is expensive to build and maintain. The methodology is tested and verified on a real trunk main network with observed turbidity measurement data. Results obtained show that the methodology can detect if discolouration material is mobilised, estimate if sufficient turbidity will be generated to exceed a preselected threshold and approximate how long the material will take to reach the downstream meter. Classification based forecasts of turbidity can be reliably made up to 5 h ahead although at the expense of increased false alarm rates. The methodology presented here could be used as an early warning system that can enable a multitude of cost beneficial proactive management strategies to be implemented as an alternative to expensive trunk mains cleaning programs.

Keywords: Data-driven; Discolouration; Forecasting; Machine learning; Trunk mains; Turbidity.

MeSH terms

  • Forecasting
  • Probability
  • Water
  • Water Quality*
  • Water Supply*

Substances

  • Water