A Multivariate Analysis of Water Quality in Lake Ghrib, Algeria

Hamil, Somia; Baha, Mounia; Arab, Sihem; Doukhandji, Nassima; Arab, Abdeslem

doi:10.1007/978-3-319-70548-4_235

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The grouping of the monitoring stations was based on statistically significant clusters in a distant connection (D link /D max ) × 100. Hierarchical clustering was achieved consistent with Ward's method as well as a squared Euclidian distance (Varol et al 2011;Hamil et al 2013;Fathi et al 2018).…”

Section: Cluster Analysismentioning

confidence: 99%

Assessment of the quality of the Densu river using multicriterial analysis and water quality index

2021

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The natural resources, especially water in the Densu river basin, play significant roles in the socio-economic development of Ghana. The purpose of this study was to analyse the water quality of the Densu river using water quality index (WQI) and multivariate techniques. In this study, physico-chemical and bacteriological parameters were measured from surface water samples taken from eight (8) sampling stations in the study area. water quality index and multivariate techniques such as hierarchical cluster analysis and principal component analysis were utilized in the analysis of surface water quality data. The results indicated that the average WQI of the Densu river for the two sampling periods was sixty-one (61) which is classified as Medium, based on the Solway WQI index. The pH levels of all the samples were within allowable limits of World Health Organization (WHO) guidelines. All the sampling stations for the two seasonal periods had bacteriological parameters higher than WHO guidelines, making the samples unsuitable for most domestic uses. The study revealed that six (6) principal components accounted for about 97% of the total variance of dataset and three (3) spatial clusters were classified. This research has provided the basis for applying both WQI and multivariate techniques in analysing and classifying water quality in a river basin.

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Section: Cluster Analysismentioning

confidence: 99%