Combining the global and local estimation models for predicting PM 10 concentrations

Abstract

This paper presents a new way of predicting timely air pollution measure such as the PM 10 concentration in Seoul based on a new method of combining the global and local estimation models. In the proposed method, the structure of nonlinear dynamics of generating air pollution data series is analyzed by investigating the attractors in the phase space and this structure is used to build the prediction model. Then, the global estimation model such as the network with Gaussian kernel functions is trained for the air pollution series data. Furthermore, the local estimation model which will recover the errors of the global estimation model using the on-line adaptation method, is also adopted. As a result, the proposed prediction model combining the global and local estimation models provides robust performances of predicting PM 10 concentrations. © Springer International Publishing AG 2017.