title: Adaptive neuro fuzzy inference system for classification of water quality status.
abstract: 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.
authors.affiliation: School of Economics and Management, Beihang University, Beijing 100191, China. yanhannn@163.com
authors.forename: Han
authors.initials: H
authors.lastname: Yan
authors.forename: Zhihong
authors.initials: Z
authors.lastname: Zou
authors.forename: Huiwen
authors.initials: H
authors.lastname: Wang
mesh_terms: D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D014867:Water / Q000592:standards*; D014881:Water Supply
chemical_list: D014867:Water