Development and Application of Water Quality Classification Models

Akbar, Tahir Ali

Development and Application of Water Quality Classification Models

atmire.migration.oldid	1322
dc.contributor.advisor	Achari, Gopal
dc.contributor.advisor	Hassan, Quazi K.
dc.contributor.author	Akbar, Tahir Ali
dc.date.accessioned	2013-09-09T20:53:26Z
dc.date.available	2013-11-12T08:00:11Z
dc.date.issued	2013-09-09
dc.date.submitted	2013	en
dc.description.abstract	Though surface water quality is a dynamic quantity; factors, such as increase in population, changes in climate, and anthropogenic activities impose more variability in recent times. The main objectives of this thesis were to: (i) develop models for classification of raw surface water quality, (ii) analyze the spatial patterns and temporal trends of surface water quality, (iii) obtain exceedances of parameters in each class; and (iv) develop remote sensing based models for Canadian Water Quality Index (CWQI) and turbidity. A methodology was developed using principal component analysis (PCA) and clustering techniques on the basis of 19 water quality parameters for 18 lakes of Alberta. Three principal components (PCs) were indicators of hardness, salinity and biological activities for lakes. The surface water quality showed deterioration as the cluster number increased from 1 to 5. The most deteriorated quality of water was found in Cardinal Lake, Moonshine Lake, Winagami Lake, Miquelon Lake and Saskatoon Lake. A total exceedance model was developed for clusterization of surface water quality for 12 major rivers of Alberta. The PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover for rivers. The clusters showed a strong relationship with CWQI classes. Snow melting deteriorated the surface water quality of rivers due to anthropogenic activities from different land uses/covers. There was increasing trend for the mean exceedance of the parameters as the cluster number increased from low to high. Empirical models were developed for Canadian Water Quality Index and turbidity using 31 scenes of Landsat-5 TM satellite data for the Bow River. The significant models were 14 for CWQI and 12 for turbidity. 100% matching was found for 72% and 83% of data in best-fit models for CWQI and turbidity respectively. The variation in the Bow River water quality was due to climatic changes and irrigation.	en_US
dc.identifier.citation	Akbar, T. A. (2013). Development and Application of Water Quality Classification Models (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25634	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/25634
dc.identifier.uri	http://hdl.handle.net/11023/932
dc.language.iso	eng
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary	en
dc.publisher.place	Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Environmental
dc.subject.classification	Principal component analysis	en_US
dc.subject.classification	Clustering	en_US
dc.subject.classification	Geographic Information System	en_US
dc.subject.classification	Pattern Recognition	en_US
dc.subject.classification	River water quality	en_US
dc.subject.classification	Alberta River	en_US
dc.subject.classification	Alberta Lakes	en_US
dc.subject.classification	Remote sensing modeling	en_US
dc.subject.classification	Surface water quality	en_US
dc.title	Development and Application of Water Quality Classification Models
dc.type	doctoral thesis
thesis.degree.discipline	Civil Engineering
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.item.requestcopy	true