@article{(Open Science Index):https://publications.waset.org/pdf/5674, title = {Parameter Sensitivity Analysis of Artificial Neural Network for Predicting Water Turbidity}, author = {Chia-Ling Chang and Chung-Sheng Liao}, country = {}, institution = {}, abstract = {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.}, journal = {International Journal of Geological and Environmental Engineering}, volume = {6}, number = {10}, year = {2012}, pages = {657 - 660}, ee = {https://publications.waset.org/pdf/5674}, url = {https://publications.waset.org/vol/70}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 70, 2012}, }