Affiliations: [a]
College of Computer Science and Technology & College of Data Science, Taiyuan University of Technology, Jinzhong, Shanxi, China
| [b]
Hangzhou City University, Hangzhou, Zhejiang, China
| [c] xup Architekten Xu und Partner, Hangzhou, Zhejiang, China
Correspondence:
[*]
Corresponding author. Zhenxing Ren, College of Computer Science and Technology & College of Data Science, Taiyuan University of Technology, Jinzhong, Shanxi, China. E-mail: renzhenxing@tyut.edu.cn.
Note: [1] This author contributed equally to this work and should be considered the co-first author.
Abstract: Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy (TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance.
Keywords: PWA model, prediction of air pollutants, spatial-temporal features, hierarchical clustering-based identification
