Research Article
Activation Force-based Air Pollution Observation Station Clustering
@INPROCEEDINGS{10.4108/eai.19-8-2015.2259688, author={Di Huang and Ni Zhang and Hong Yu and Huanyu Zhou and Zhanyu Ma and Weisong Hu and Jun Guo}, title={Activation Force-based Air Pollution Observation Station Clustering}, proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={EAI}, proceedings_a={QSHINE}, year={2015}, month={9}, keywords={air pollution subnetworks activiation force clustering}, doi={10.4108/eai.19-8-2015.2259688} }- Di Huang
Ni Zhang
Hong Yu
Huanyu Zhou
Zhanyu Ma
Weisong Hu
Jun Guo
Year: 2015
Activation Force-based Air Pollution Observation Station Clustering
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2259688
Abstract
With huge amount of observed air quality and components data, it is of great challenge to analyze and trace the pollutant diffusion path. Partitioning the air pollution sources (air quality observation stations) into subnetworks will help a lot in tracing the air pollution diffusion path. Conventional air pollution sources clustering methods, which are based on geography or pollutant levels, present weak correlation with pollution transmission links. In order to overcome such problem, a method of air pollution sources clustering via activation force (AF) model is introduced in this paper. We model the connections of the pollution sources by AF so that the relationship among the observation stations and the coincidence of the transmission links can be modeled effectively. With the affinity matrix obtained via AF modeling, we conduct clustering of the air pollution sources via modularity measurement. Compared to K-means clustering method purely, which is based on the air quality index of pollutants, the proposed approach shows several advantages in air pollution network clustering.
