GENII: A graph neural network-based model for citywide litter prediction leveraging crowdsensing data

Abstract

Plastic litter and its associated environmental hazards have garnered global attention in recent years, highlighting the need for effective management. Improper handling of plastic litter can lead to environmental degradation, making it crucial to address this issue. In this paper, we propose an innovative approach to predict the spatial distribution and quantity of plastic litter at the city level by leveraging crowd-sensing data and designing a graph neural network-based model. Meteorological data is specifically used to enhance temporal correlation, while the spatial distribution of litter is clustered using the K-means method to capture spatial correlation. For each cluster, multiple graphs are constructed based on the points of interest (POIs) and litter distribution within the cluster. Graph attention neural networks and heterogeneous graph attention networks are then utilized to aggregate the adjacency information and overall structural information of these graphs, respectively. Finally, the litter prediction results are obtained through multiple fully connected layers. Real-world experiments convincingly demonstrate the high effectiveness of our proposed model in predicting city-wide litter, surpassing multiple comparative models. All the code and datasets can be accessed from the GitHub repository at https://github.com/ZJUDataIntelligence/Genii.