Main content

Characterizing Risk through Water Safety Plans and Investigating Drinking Water Advisories in First Nations Communities using Probabilistic Neural Networks

Show simple item record

dc.contributor.advisor McBean, Edward
dc.contributor.author Post, Yvonne
dc.date.accessioned 2017-09-05T15:04:51Z
dc.date.available 2018-08-21T05:00:23Z
dc.date.copyright 2017-08
dc.date.created 2017-08-21
dc.date.issued 2017-09-05
dc.identifier.uri http://hdl.handle.net/10214/11503
dc.description.abstract Safe clean drinking water is a basic human need and yet many communities face challenges in providing clean water to their population. This research looks to address issues in drinking water treatment systems from two different perspectives, first taking a local level approach in the water system itself, then looking at trends in the occurrence, frequency, duration, and cause of drinking water advisories (DWAs) in First Nations communities across Canada. A risk assessment template for identifying hazards in a drinking water system, from source, through treatment and distribution, to the consumer, was evaluated, and a condensed version was developed which was more robust at identifying higher risk areas of the system. Next, an artificial neural network model is used to identify different key factors affecting DWAs in different provinces across Canada, suggesting that a Canada-wide approach is not adequate to reduce DWAs. en_US
dc.description.sponsorship Natural Sciences and Engineering Research Council, Res'Eau National Centre of Excellence in small drinking water systems en_US
dc.language.iso en en_US
dc.subject water safety plan en_US
dc.subject risk assessment en_US
dc.subject probabilistic neural network en_US
dc.subject drinking water advisory en_US
dc.subject First Nations en_US
dc.title Characterizing Risk through Water Safety Plans and Investigating Drinking Water Advisories in First Nations Communities using Probabilistic Neural Networks en_US
dc.type Thesis en_US
dc.degree.programme Engineering en_US
dc.degree.name Master of Applied Science en_US
dc.degree.department School of Engineering en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


Files in this item

Files Size Format View Description
Post_Yvonne_201708_MASc.pdf 1.363Mb PDF View/Open Main thesis

This item appears in the following Collection(s)

Show simple item record