Abstract
AbstractOnline monitoring of multivariate water quality data is becoming a practical means of improving distribution network management and meeting water security goals. Changes in water quality are often due to changes in the hydraulic operations of the network. These operational changes create patterns of water quality change that are similar, but not exactly the same, from one instance to the next. Classification of multivariate change patterns through trajectory clustering is introduced in this paper to create a pattern library from historical water quality data and as an online process with the goal of reducing false positive water quality event detections. Prior to event declaration, a short sequence of the preceding multivariate data is compared against the pattern library to assess its similarity to a previously observed pattern. A fuzzy clustering algorithm is utilized to assign multivariate pattern memberships for water quality patterns associated with water quality events in both the offline an...
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Water Resources Planning and Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
