Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference
Keywords:AI For Social Impact (AISI Track Papers Only), Humans And AI (HAI)
AbstractAir pollution is a global problem and severely impacts human health. Fine-grained air quality (AQ) monitoring is important in mitigating air pollution. However, existing AQ station deployments are sparse. Conventional interpolation techniques fail to learn the complex AQ phenomena. Physics-based models require domain knowledge and pollution source data for AQ modeling. In this work, we propose a Gaussian processes based approach for estimating AQ. The important features of our approach are: a) a non-stationary (NS) kernel to allow input depended smoothness of fit; b) a Hamming distance-based kernel for categorical features; and c) a locally periodic kernel to capture temporal periodicity. We leverage batch-wise training to scale our approach to a large amount of data. Our approach outperforms the conventional baselines and a state-of-the-art neural attention-based approach.
How to Cite
Patel, Z. B., Purohit, P., Patel, H. M., Sahni, S., & Batra, N. (2022). Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12080-12088. https://doi.org/10.1609/aaai.v36i11.21467
AAAI Special Track on AI for Social Impact