SVR based dense air pollution estimation model using static and wireless sensor network

Abstract

In this paper we introduce a new SVR (Support Vector Regression) based model for estimating air pollution at fine spatial granularity. Specifically we use historical data from (sparse) government monitoring sites and a (dense) wireless sensor network, along with SVR — a supervised regression learning method to estimate an air pollution surface for any given hour on any given day in Sydney. Further, we conduct trials and results which show that air pollution estimations from our estimation system has a high spatial resolution, and is more accurate than estimations from an ANN (Artificial Neural Network) model. Our model can benefit further medical studies on estimating dosage and its relation to health outcomes.