%0 Journal Article
	%A S. Wechmongkhonkon and  N.Poomtong and  S. Areerachakul
	%D 2012
	%J International Journal of Environmental and Ecological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 69, 2012
	%T Application of Artificial Neural Network to Classification Surface Water Quality
	%U https://publications.waset.org/pdf/9127
	%V 69
	%X Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
	%P 574 - 578