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- Title
A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection.
- Authors
Martinez-Camara, M.; Béjar Haro, B.; Stohl, A.; Vetterli, M.
- Abstract
Emissions of harmful substances into the atmosphere are a serious environmental concern. In order to understand and predict their effects, it is necessary to estimate the exact quantity and timing of the emissions from sensor measurements taken at different locations. There are a number of methods for solving this problem. However, these existing methods assume Gaussian additive errors, making them extremely sensitive to outlier measurements. We first show that the errors in real-world measurement data sets come from a heavy-tailed distribution, i.e., include outliers. Hence, we propose robustifying the existing inverse methods by adding a blind outlier-detection algorithm. The improved performance of our method is demonstrated on a real data set and compared to previously proposed methods. For the blind outlier detection, we first use an existing algorithm, RANSAC, and then propose a modification called TRANSAC, which provides a further performance improvement.
- Subjects
EMISSIONS (Air pollution); OUTLIER detection; GAUSSIAN processes; NUCLEAR power plants; ATMOSPHERIC models
- Publication
Geoscientific Model Development, 2014, Vol 7, Issue 5, p2303
- ISSN
1991-959X
- Publication type
Article
- DOI
10.5194/gmd-7-2303-2014