@article{(Open Science Index):https://publications.waset.org/pdf/7227,
	  title     = {Using Radial Basis Function Neural Networks to Calibrate Water Quality Model},
	  author    = {Lihui Ma and  Kunlun Xin and  Suiqing Liu},
	  country	= {},
	  institution	= {},
	  abstract     = {Modern managements of water distribution system
(WDS) need water quality models that are able to accurately predict
the dynamics of water quality variations within the distribution system
environment. Before water quality models can be applied to solve
system problems, they should be calibrated. Although former
researchers use GA solver to calibrate relative parameters, it is
difficult to apply on the large-scale or medium-scale real system for
long computational time. In this paper a new method is designed
which combines both macro and detailed model to optimize the water
quality parameters. This new combinational algorithm uses radial
basis function (RBF) metamodeling as a surrogate to be optimized for
the purpose of decreasing the times of time-consuming water quality
simulation and can realize rapidly the calibration of pipe wall reaction
coefficients of chlorine model of large-scaled WDS. After two cases
study this method is testified to be more efficient and promising, and
deserve to generalize in the future.},
	    journal   = {International Journal of Environmental and Ecological Engineering},
	  volume    = {2},
	  number    = {2},
	  year      = {2008},
	  pages     = {9 - 17},
	  ee        = {https://publications.waset.org/pdf/7227},
	  url   	= {https://publications.waset.org/vol/14},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 14, 2008},
	}