TY - JOUR
T1 - Adaptive neuro fuzzy inference system for classification of water quality status
AU - Yan, Han
AU - Zou, Zhihong
AU - Wang, Huiwen
PY - 2010/12
Y1 - 2010/12
N2 - An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set (nine weeks, total 845 observations) was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.
AB - An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set (nine weeks, total 845 observations) was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.
KW - Adaptive neuro fuzzy inference system
KW - Artificial neural networks
KW - Classification
KW - Water quality status
UR - https://www.scopus.com/pages/publications/78649912654
U2 - 10.1016/S1001-0742(09)60335-1
DO - 10.1016/S1001-0742(09)60335-1
M3 - 文章
C2 - 21462706
AN - SCOPUS:78649912654
SN - 1001-0742
VL - 22
SP - 1891
EP - 1896
JO - Journal of Environmental Sciences (China)
JF - Journal of Environmental Sciences (China)
IS - 12
ER -