Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment
作者： Ado Abdu , Adamu Mustapha
DOI:
关键词: Biochemical oxygen demand 、 Total dissolved solids 、 Chemistry 、 Environmental chemistry 、 Chemical oxygen demand 、 Suspended solids 、 Water quality 、 Surface water 、 Turbidity 、 Varimax rotation
摘要: Principal component analysis (PCA) and multiple linear regressions were applied on the surface water quality data with aim of identifying pollution sources their contribution toward variation. Surface samples collected from four different sampling points along Jakara River. Fifteen physico-chemical parameters selected for analysis: dissolved oxygen (DO), biochemical demand (BOD 5 ), chemical (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in form ammonia (NH 3 turbidity, (DS), total (TS), nitrates (NO chloride (Cl) phosphates (PO 4 3- ). PCA was used to investigate origin each yielded five varimax factors 83.1% variance addition identified latent namely: ionic, erosion, domestic, dilution effect agricultural run-off. Multiple variable significant value (r 0.970, R 2 0.942, p < 0.01). Keywords: River, Stepwise regression, Varimax factor, rotation, Water
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