Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
This addresses a domain-specific problem for environmental monitoring by extending the applicability of deep-learning models beyond their training domains, though it appears incremental as it builds on existing deep-learning and domain-decomposition techniques.
The paper tackles the limitation of deep-learning techniques in air-pollution forecasting, which were restricted to domains where traditional PDE solvers apply, by introducing a framework that enables training across different model domains and reduces run-time by two orders of magnitude.
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.