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.
Han Yan,Zhihong Zou,Huiwen Wang School of Economics and Management,Beihang University,Beijing 100191,China
城市经济发展带来了产业结构和用水结构的明显变化,本文分析了用水结构受产业结构变化影响的变动规律。用水结构和产业结构是一系列按照时间顺序排列的成分数据,运用软件S IM CA-P 11.5建立了基于偏最小二乘回归的成分数据线性回归模型,证明了用水结构组分变动与产业结构变动有一致性。为城市水资源管理提供了合理有效的定量方法,并可作为对未来用水结构预测的基础。
In order to understand the dynamic change of water quality in a specific period of time,a type of possibility transition matrix based on the theory of Markov process was established.The transition possibility with a weight to calculate the degree of absolute advancement was given based on the result of water quality evaluation.The concept of relative advancement was presented.It was used to evaluate the extent of water quality changed in a period of time.The method was used to calculate the degrees of relative advancement for 4 rivers in China,and the results were analyzed.
YUN Yi,ZOU Zhihong,FENG Wei,RU Mai School of Economics and Management,Beihang University,Beijing 100191,China.
