Air quality analysis of Sichuan province based on complex network and CSP algorithm

Abstract

The air quality is directly related to people’s lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

Research on complex network modeling of building materials supply and demand and characteristics of communities

“…A relatively good result will lead to nodes inside the community with a high degree of similarity, while those outside the community have a low degree of similarity. Scholars have used communities to analyze problems in real-life scenarios[ .18 ] [ 19 ]. By identifying and categorizing communities in the complex network of building materials, we can better understand the relationship between the supply and demand of building materials, and reveal the potential laws hidden in the network structure.…”