TY - JFULL AU - S. Wechmongkhonkon and N.Poomtong and S. Areerachakul PY - 2012/10/ TI - Application of Artificial Neural Network to Classification Surface Water Quality T2 - International Journal of Environmental and Ecological Engineering SP - 573 EP - 578 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/9127 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 69, 2012 N2 - 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. ER -