Deep Gaussian Processes for Air Quality Inference

• URL: http://arxiv.org/abs/2211.10174v1
• Date: Fri, 18 Nov 2022 11:42:42 GMT
• Title: Deep Gaussian Processes for Air Quality Inference
• Authors: Aadesh Desai, Eshan Gujarathi, Saagar Parikh, Sachin Yadav, Zeel Patel, Nipun Batra
• Abstract summary: Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. This work demonstrates that Deep Gaussian models (DGPs) are a promising model for the task of inference.
• Score: 3.2687545875380186
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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