Extracting Value From Complex High-Frequency Multivariate Water Quality Data: Exploring Routinely Collected Operational Data

8 pages•Published: September 20, 2018

Josie Ashe, Emilie Grand-Clement, David Smith, Richard E. Brazier and Dragan A. Savic

Abstract

Drinking water treatment works are increasingly placed under external stressors including climatic variability, land use and management, and pollution incidents. Routine high-frequency water quality monitoring is an integral part of operational control and is used to inform the treatment process and support the identification of risks. However, in order to improve decision making using the complex, time-series of water quality data that are generated (and typically archived), there must be distinction between basic sensor errors, artefacts of system design and management, and process driven patterns. This paper explores these complex data in order to support synthesis of uncleaned (or raw), high-frequency data; extracting information value from routine catchment wide monitoring. The data are presented in a form that enhances the capability and capacity to utilise existing complex data; improves understanding of complex surface water systems; and helps facilitate data driven models to investigate and forecast the dynamics between water quality determinands during hard-to-treat spate (or rainfall-runoff) events.

Keyphrases: high frequency monitoring, information value, multivariate, visualization, water quality

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 103-110.

Links:	https://easychair.org/publications/paper/9m4v
	https://doi.org/10.29007/g29m

BibTeX entry

@inproceedings{HIC2018:Extracting_Value_From_Complex,
  author    = {Josie Ashe and Emilie Grand-Clement and David Smith and Richard E. Brazier and Dragan A. Savic},
  title     = {Extracting Value From Complex High-Frequency Multivariate Water Quality Data: Exploring Routinely Collected Operational Data},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/9m4v},
  doi       = {10.29007/g29m},
  pages     = {103-110},
  year      = {2018}}

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