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A Robust Method for Inverse Transport Modeling of Atmospheric Emissions Using Blind Outlier Detection : Volume 7, Issue 5 (10/10/2014)

By Martinez-camara, M.

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Book Id: WPLBN0003984193
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: A Robust Method for Inverse Transport Modeling of Atmospheric Emissions Using Blind Outlier Detection : Volume 7, Issue 5 (10/10/2014)  
Author: Martinez-camara, M.
Volume: Vol. 7, Issue 5
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Description: School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. 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.

A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection

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