Trend Evaluation Of Raw Water Quality From Kpong Dam (Volta River) Using Statiscal Analysis And Time Series Prediction Model

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dc.contributor.author Bakomnaah, B.
dc.date.accessioned 2018-11-20T16:34:58Z
dc.date.available 2018-11-20T16:34:58Z
dc.date.issued 2018-07
dc.identifier.uri http://ugspace.ug.edu.gh/handle/123456789/25611
dc.description.abstract This research evaluates raw water quality status from Kpong dam of the Volta River, Eastern part of Ghana, to assess its quality levels over a six-year period and predict future quality trend. The dataset identified twenty-six physicochemical constituents in each monthly sample analyzed. Hydrochemical analysis showed variation in contaminant levels for some constituents which ranged from low to considerable contamination (WHO, 2006). PO42-levels were relatively high. Results of WQI indicated general quality degradation with minimal variations across the years with respect to surface water quality rating criteria (WC,2003). The forecasted figures with the fitted ARIMA (3, 1, 1) model also suggest minimal degradation of the water quality in the light of the current prevailing conditions. It is therefore recommended that efforts be made to prevent further pollution of the Volta River. en_US
dc.language.iso en en_US
dc.publisher University of Ghana en_US
dc.subject Kpong dam en_US
dc.subject Eastern part of Ghana en_US
dc.subject dataset identified twenty-six physicochemical constituents en_US
dc.subject Hydrochemical analysis en_US
dc.subject contaminant levels en_US
dc.subject considerable contamination en_US
dc.subject WQI indicated general quality degradation en_US
dc.subject surface water quality rating criteria en_US
dc.subject suggest minimal degradation en_US
dc.subject water quality en_US
dc.subject current prevailing conditions en_US
dc.subject prevent further pollution en_US
dc.subject Volta River. en_US
dc.title Trend Evaluation Of Raw Water Quality From Kpong Dam (Volta River) Using Statiscal Analysis And Time Series Prediction Model en_US
dc.type Thesis en_US


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