Deep Gaussian Processes for Air Quality Inference

TL;DR

This paper explores the use of Deep Gaussian Processes with Doubly Stochastic Variational Inference for accurate, fine-grained air quality inference in areas with sparse monitoring stations, showing promising results.

Contribution

It introduces a DGP-based approach with a specific inference algorithm for air quality inference, demonstrating competitive performance.

Findings

DGP models perform comparably to state-of-the-art methods

Doubly Stochastic Variational Inference is effective for DGPs

The approach improves air quality estimation in unmonitored areas

Abstract

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.