Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting

TL;DR

This paper introduces a deep-learning framework that significantly extends the range and reduces the runtime of air-pollution forecasting models by integrating domain-decomposition techniques, enabling training across multiple domains.

Contribution

The work presents a novel combination of deep learning and domain-decomposition methods to enable model training across different domains and extend deployment beyond initial training areas.

Findings

Reduces forecasting runtime by two orders of magnitude.

Enables training across multiple model domains.

First implementation combining deep learning with domain decomposition.

Abstract

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-learning techniques were limited to domains, where the PDE solver was applicable. We demonstrate a deep-learning framework for air-pollution monitoring and forecasting that provides the ability to train across different model domains, as well as a reduction in the run-time by two orders of magnitude. It presents a first-of-a-kind implementation that combines deep-learning and domain-decomposition techniques to allow model deployments extend beyond the domain(s) on which the it has been trained.