Research on Pollution Prediction Model Based on Pollutant Knowledge Graph

School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China

^* Corresponding author’s e-mail: zhaoyw@ucas.ac.cn

Abstract

At present, the main conventional method to study pollution diffusion is to establish a mathematical model to analyse the relationship between pollution sources. The lack of analysis on the characteristics and attributes of pollution sources leads to unsatisfactory identification results. To solve this problem, a pollution diffusion prediction method based on pollution atlas was proposed. The pollution knowledge graph was constructed to describe the relationship between pollution source attributes and the interaction and influence between pollution source and region. The diffusion characteristics of pollution were extracted from the atlas, and DBSCAN was used to achieve clustering and deep neural network model was built to predict the pollution degree at different depths. The model is verified in a typical contaminated site. The experimental results show that the proposed method is effective and has good recognition effect and convergence speed.