Risk Assessment of Atmospheric Hazard Releases Using K-Means Clustering

Abstract

Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds describes a distinct area at risk from potentially hazardous atmospheric contamination. Overimposing the resulting risk areas with ground maps, it is possible to assess the impact of the population exposure to the contaminants. The releases were simulated in the Bosphorus channel. Simulations were performed for one year at weekly interval, both day and night, to sample all different potential atmospheric conditions.