Predicting building contamination using machine learning

Abstract

Potential events involving biological or chemical contamination of buildings are of major concern in the area of homeland security. Tools are needed to provide rapid, on- site predictions of contaminant levels given only approximate measurements in limited locations throughout a building. In principal, such tools could use calculations based on physical process models to provide accurate predictions. In practice, however, physical process models are too complex and computationally costly to be used in a real-time scenario. In this paper, we investigate the feasibility of using machine learning to provide easily computed but approximate models that would be applicable in the field. We develop a machine learning method based on support vector machine regression and classification. We apply our method to problems of estimating contamination levels and contaminant source location.