J
Joby Boxall
Researcher at University of Sheffield
Publications - 184
Citations - 4890
Joby Boxall is an academic researcher from University of Sheffield. The author has contributed to research in topics: Water quality & Environmental science. The author has an hindex of 34, co-authored 169 publications receiving 3904 citations. Previous affiliations of Joby Boxall include University of Warwick & University of Cambridge.
Papers
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Journal ArticleDOI
Discolouration in potable water distribution systems: a review.
Jan Vreeburg,Joby Boxall +1 more
TL;DR: There are very few published practicable tools and techniques available to aid water companies in the planned management and control of discolouration problems, and this is an area in need of significant further practical research and development.
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Influence of hydraulic regimes on bacterial community structure and composition in an experimental drinking water distribution system.
TL;DR: Bacteria inhabiting biofilms, predominantly species belonging to genera Pseudomonas, Zooglea and Janthinobacterium, have an enhanced ability to express extracellular polymeric substances to adhere to surfaces and to favour co-aggregation between cells than those found in the bulk water.
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Methodological approaches for studying the microbial ecology of drinking water distribution systems
TL;DR: The currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated and will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects.
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Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows
TL;DR: The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level.
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Novelty detection for time series data analysis in water distribution systems using support vector machines
TL;DR: Support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data and the robustness derives from the training error function is applied to a case study.