Detection and Localization of Harmful Atmospheric Releases via Support Vector Machines
نویسندگان
چکیده
Background: We present a Support Vector Machine (SVM) approach to the localization of hazardous particulate releases in an urban area using features constructed only from measurements obtained from a network of sensors. Results: We find high levels of localization accuracy when a reasonable number of noisy sensors are deployed within the environment. We also compare SVM source localization performance to an existing stochastic localization technique over varying degrees of sensor noise and find it favorable for areas prone to urban canyon turbulence effects. Conclusions: This approach is in contrast to earlier works which either use solutions to inverse dispersion problems for localization or apply maximum likelihood techniques. By using established SVM results, we also tackle the problems of release detection and optimal sensor placement.
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