Assessment of river water quality using uncertainly mathematical model: A case Study of Yellow River, China

In recent years, there is a growing tendency to use socalled uncertainly mathematical model, fuzzy set theory, a type of nonlinear and primarily data-driven approach and, an artificial neural network (ANN) to complement traditional water quality evaluation. Since the proper identification of water quality conditions in river system based on limited observations is an essential task for meeting the goals of environmental management, the assessment results obtained from the using conventional methodologies may easily mislead or bias the decision makers or managers owing to inherent imprecision of conventional methodologies and the uncertainty in the river environment. In this research, two complementary evaluation methods, fuzzy synthetic evaluation (FSE) and self-organizing map (SOM) are applied to assess river water quality conditions and their application potential and occasions are also compared with a case study for the Baiyin section of Yellow River in China.