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- W2893457431 abstract "Abstract In this paper, we use ordinary kriging, a geostatistical method to create prediction surfaces for 11 nutrients in the surface sediments of Lake Chivero, Zimbabwe within a GIS environment. Interpolation utilised 120 sampling points in the lake. Although a normal distribution in the data is recommended for kriging, non-normal data was preferred for all datasets in this study. For all models developed, cross validation results showed bias acceptable for the creation of prediction surfaces. Also, the correlation between Coefficient of Variation (CV%) of the different data sets and model accuracy in terms of Relative Root Mean Standardised Error (RRMSE) was strong (rho = 0.936) and significant (p" @default.
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- W2893457431 date "2018-12-01" @default.
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- W2893457431 title "Spatial distribution of surface sediment nutrients of a subtropical hypereutrophic lake: Lake Chivero, Zimbabwe" @default.
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- W2893457431 doi "https://doi.org/10.1016/j.enmm.2018.09.006" @default.
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