Advances in Environmental Research, volume 10 by Justin A. Daniels

By Justin A. Daniels

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By Justin A. Daniels

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This allows the conversion of such data into a more representative form, yielding greater accuracy and validity than alternative methods. Consequently Stacey‘s (2005) method of distribution fitting was employed allowing for the factors to be interpreted as:    Critically important Important Unimportant Factors are thus able to be ranked against one another and the t-test applied in order to determine the significance. A confidence interval of 99% was used as the consequence of wrongly rejecting the null hypothesis was considered serious.

G. Local Polynomial Interpolation). Point vs. g. weather stations and oil-well readings). g. given population counts). Gradual vs. g. g. Artificial Neural Networks). 4. SPATIAL INTERPOLATION TECHNIQUES Spatial interpolation techniques are widely used in the field of environmental engineering as shown in Table 1. This section discusses the available interpolation techniques, rendering details about the implementation, specific advantages and disadvantages, and the scenario in which it shows the best interpolation performance.

Repetto, R. (2001). , Policy Sciences, 34(3/4), 303-327. Schmidt, C. W. (2009). Carbon Offsets, Environmental Health Perspectives, 117(2), A62A68. Schneider, M. (2008a). All about positioning: Keeping a close eye on how firms are coping in a world that‘s become more carbon constrained, The Financial Mail, 17 Oct, 15. Schneider, M. (2008b). Clean Development Mechanism: Green Money up for grabs, The Financial Mail, 17 Oct, 10. Shrestha, R. M. (2004). Technological Implications of the Clean Development Mechanism for the Power Sector in Three Asian Countries, International Review for Environmental Strategies, 5, 273-288.

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