Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.
@article{arxiv.2211.10174,
title = {Deep Gaussian Processes for Air Quality Inference},
author = {Aadesh Desai and Eshan Gujarathi and Saagar Parikh and Sachin Yadav and Zeel Patel and Nipun Batra},
journal= {arXiv preprint arXiv:2211.10174},
year = {2022}
}
Comments
Accepted for publication at ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2023)