Systematically Modeling the Interactions among Multiple Indicators While Considering the Structure of a River Network

Abstract

Multiple water quality-related indicators in surface water involve many interactions. However, there is still no global interactive landscape considering the river network (up- and downstream relationship). Fuzzy cognitive maps (FCMs) are a type of quantitative method that conducts the time series prediction while considering the interaction among different variables (including self-impact). Additionally, FCMs enable the analysis of variable interactions across different locations, such as the monitoring sites within a surface water network (e.g., river network). In this study, we utilized the global monitoring data from 49 stations (sites) along the Qu River, Fu River, and Jialing River, Hechuan, Chongqing, China (February 1, 2021, to March 9, 2024), to construct a global map illustrating the interactions among the indicators across all of these sites. The analyzed results provide insights to infer the interaction between any pairs of variables and predict the amount of variables in future time stamps. The interstation and intrastation relationships were analyzed from three perspectives: simple path, cycle, and degree derived from the FCM-produced graph. Concrete interactions were quantified using edge weights in the graph to uncover the causes of pollution and understand the hidden trends in the data.