Optimising Placement of Pollution Sensors in Windy Environments

Abstract

Air pollution is one of the most important causes of mortality in the world. Monitoring air pollution is useful to learn more about the link between health and pollutants, and to identify areas for intervention. Such monitoring is expensive, so it is important to place sensors as efficiently as possible. Bayesian optimisation has proven useful in choosing sensor locations, but typically relies on kernel functions that neglect the statistical structure of air pollution, such as the tendency of pollution to propagate in the prevailing wind direction. We describe two new wind-informed kernels and investigate their advantage for the task of actively learning locations of maximum pollution using Bayesian optimisation.

Presented at the AI for Earth Sciences Workshop at Advances in Neural Information Processing Systems (NeurIPS) 2020. Updated August 2022 to correct scale of y axis on distance plots

References in corpus (2)

• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
• Machine Learning for a Low-cost Air Pollution Network

Cited by in corpus (1)

• Bayesian Optimisation for Active Monitoring of Air Pollution