WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/3706,
	  title     = {Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water},
	  author    = {S. Areerachakul},
	  country	= {},
	  institution	= {},
	  abstract     = {Nowadays, several techniques such as; Fuzzy
Inference System (FIS) and Neural Network (NN) are employed for
developing of the predictive models to estimate parameters of water
quality. The main objective of this study is to compare between the
predictive ability of the Adaptive Neuro-Fuzzy Inference System
(ANFIS) model and Artificial Neural Network (ANN) model to
estimate the Biochemical Oxygen Demand (BOD) on data from 11
sampling sites of Saen Saep canal in Bangkok, Thailand. The data is
obtained from the Department of Drainage and Sewerage, Bangkok
Metropolitan Administration, during 2004-2011. The five parameters
of water quality namely Dissolved Oxygen (DO), Chemical Oxygen
Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen
(NO3N), and Total Coliform bacteria (T-coliform) are used as the
input of the models. These water quality indices affect the
biochemical oxygen demand. The experimental results indicate that
the ANN model provides a higher correlation coefficient (R=0.73)
and a lower root mean square error (RMSE=4.53) than the
corresponding ANFIS model.},
	    journal   = {International Journal of Environmental and Ecological Engineering},
	  volume    = {6},
	  number    = {4},
	  year      = {2012},
	  pages     = {168 - 172},
	  ee        = {https://publications.waset.org/pdf/3706},
	  url   	= {https://publications.waset.org/vol/64},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 64, 2012},
	}