| dc.description.abstract | Detecting environmental crimes is a crucial issue for environmental protection, involving problems related to illegal disposal and burial of waste, resulting in environmental pollution, as well as earning illegal profits through false reporting of waste. Traditionally, basic mass balance, statistical information analysis, or machine learning have been used to identify suspicious data to determine whether false reporting exists. However, using mathematical and statistical methods like mass balance and numerical reports for data analysis and inspection requires checking data for target enterprises, which may miss potentially suspicious enterprises. Furthermore, once an enterprise is convicted or fined, they are likely to falsify data in the future to evade mass balance inspections. Machine learning model training often ignores temporal information and may lack consideration of upstream and downstream relationships.
To address these issues, in this paper, we presented a novel approach for environmental anomaly detection using Graph Convolutional Networks (GCNs). In our experiments, we propose the architectures of two graph convolutional network models, compare their performance, and analyze the possible factors influencing prediction results. This paper is the first to introduce deep learning to construct upstream and downstream relationships in environmental data, retaining temporal information for environmental crime detection. Our main contributions are the following: firstly, we are the pioneers in utilizing GCNs for detecting anomalies in environmental data; secondly, we constructed complex data into a graph using the Undirected Neighbor Propagation algorithm; and thirdly, we conducted a comparative analysis between GCNs with and without temporal considerations. | en_US |