Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Abstract

Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH3–N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH3–N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level Ⅳ and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57–44.58 μg/L, 10–122.50 μg/L and 2–28.67 μg/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management.