The rescaling of responsibilities in water governance in South Africa has enabled strong water services authorities, such as the eThekwini Water and Sanitation Unit (EWS) in eThekwini Municipality, to play a leading role in shaping water and sanitation policy in South Africa. Yet water governance in the city is complex, shaped by the interactions of multiple social, economic, political and environmental relations in a transforming, fast-growing city that still reflects the legacy of apartheid. This paper identifies and explores the four dominant water governance discourses evident at present in the municipality, namely “water as a human right”, “water as an economic good”, “the spatial differentiation of service provision” and finally, “experimental governance and incremental learning”, which frame the current approach adopted by EWS. These discourses provide the context for the reforms undertaken in water and sanitation provision post-apartheid in eThekwini Municipality.
A genetic ,'dgorithm is used to select the inputs to A neural network function ApproximAtor. lit the application considered, modeling criticM parameters of the Space Shuttle Main Engine (SSME), the functional rel,_tionslfip between mea._ured parameters is unknown and coxuplex. Furthermore, the number of possible input parameters is quite large. MAlty approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due the optimization altd space searching capabities of genetic Mgorithms they were employed in this paper to systematize the input selection process. The results suggest that the genetic Mgorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
process at lag m rated power level space shuttle main engine sampling interval in seconds input variable at time t discrete time function output variable at time t
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