Stochastic Local-to-Global Methods for Air Quality Prediction

Here we discuss the design of mathematical models for predicting air quality based on the empirical topology of regional atmospheric chemistry data. First, Markov chain model trained on historical local data predicts air pollution levels, evaluated by a fuzzy comprehensive decision maker. Secondly, a singular vector machine (SVM) is trained to identify conditions leading to higher pollution levels. Additionally, we describe local-to-global methods for estimation of air quality over a region containing non-uniformly-distributed data points. Together these techniques forecast air quality over an entire region, based on the current state of the atmosphere (and historical training data) from heterogeneously-distributed geographically-disjoint measurement locations.