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dc.contributor.advisorBrown, Chris
dc.contributor.authorFogelman, Shoshana
dc.date.accessioned2018-01-23T02:25:47Z
dc.date.available2018-01-23T02:25:47Z
dc.date.issued2011
dc.identifier.doi10.25904/1912/3078
dc.identifier.urihttp://hdl.handle.net/10072/366072
dc.description.abstractAccurate, continuous, real-time water quality information is becoming of paramount importance to ensure safe supplies of potable water are available, as worldwide contamination of our freshwater resources increases and we diversify our reliance on alternative water resources. Nevertheless, the acquisition of continuous reliable data for aqueous environments has proven difficult to achieve, as the majority of on-line monitoring technologies currently employed are based on direct adaptations of traditional wet laboratory methods, which were not originally designed for field or continuous monitoring applications. Consequently, they have the inherent problem of requiring strictly controlled measurement conditions, which are rarely present in the natural environment and suffer from issues such as signal drift, reagent consumption, calibration and sample pre-treatment. Seeing that consistent measurement conditions are rarely present in real world environments, direct sensor deployment often means the sensor is unavoidably exposed to a wide range of measurement conditions leading to measurement errors and invalidating the operating conditions required for reliable performance.
dc.languageEnglish
dc.publisherGriffith University
dc.publisher.placeBrisbane
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
dc.subject.keywordsDrinking water quality anaysis
dc.subject.keywordsDrinking water quality monitoring
dc.subject.keywordsNeural computing techniques
dc.titleDevelopment of a New Calibration Method for the Continuous On-Line Analysis and Monitoring of Drinking Water Quality Based on Neural Computing Techniques
dc.typeGriffith thesis
gro.facultyScience, Environment, Engineering and Technology
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorLee, Joe
dc.rights.accessRightsPublic
gro.identifier.gurtIDgu1337572807297
gro.source.ADTshelfnoADT0
gro.source.GURTshelfnoGURT1193
gro.thesis.degreelevelThesis (PhD Doctorate)
gro.thesis.degreeprogramDoctor of Philosophy (PhD)
gro.departmentGriffith School of Environment
gro.griffith.authorFogelman, Shoshana


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