Spatio-Temporal Field Neural Networks for Air Quality Inference

arXiv:2403.02354v1 Announce Type: new
Abstract: The air quality inference problem aims to utilize historical data from a limited number of observation sites to infer the air quality index at an unknown location. Considering the sparsity of data due to the high maintenance cost of the stations, good inference algorithms can effectively save the cost and refine the data granularity. While spatio-temporal graph neural networks have made excellent progress on this problem, their non-Euclidean and discrete data structure modeling of reality …

abstract air quality algorithms arxiv cost cs.ai cs.lg data good historical data index inference location maintenance networks neural networks observation quality save sparsity temporal type