School of Economics and Management, Beihang University, Beijing 100191, China.
J Environ Sci (China). 2010;22(12):1891-6. doi: 10.1016/s1001-0742(09)60335-1.
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.
采用自适应神经模糊推理系统对河流水质状况进行分类。该系统应用了溶解氧、化学需氧量和氨氮等多项物理和无机化学指标。从中国各大流域的 100 个监测站收集了一个数据集（共 845 个观测值，持续 9 周），用于训练和验证模型。该模型的正确分类率高达 89.59%。与人工神经网络相比，该模型具有更强的竞争力。它适用于水质状况的评估和分类。
