%0 Journal Article
	%A Chia-Ling Chang and  Chung-Sheng Liao
	%D 2012
	%J International Journal of Geological and Environmental Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 70, 2012
	%T Parameter Sensitivity Analysis of Artificial Neural Network for Predicting Water Turbidity
	%U https://publications.waset.org/pdf/5674
	%V 70
	%X The present study focuses on the discussion over the
parameter of Artificial Neural Network (ANN). Sensitivity analysis is
applied to assess the effect of the parameters of ANN on the prediction
of turbidity of raw water in the water treatment plant. The result shows
that transfer function of hidden layer is a critical parameter of ANN.
When the transfer function changes, the reliability of prediction of
water turbidity is greatly different. Moreover, the estimated water
turbidity is less sensitive to training times and learning velocity than
the number of neurons in the hidden layer. Therefore, it is important to
select an appropriate transfer function and suitable number of neurons
in the hidden layer in the process of parameter training and validation.
	%P 657 - 660