Air quality forecasting using SVR with quasi-linear kernel

Air pollution has threaten people's health. It is urgent for the government to strengthen and enhance air pollution monitoring capacity. In this paper, we propose an air quality prediction model to infer air pollutant concentrations, such as CO, NO_x, NO₂. The idea is to design a sophisticate piecewise linear model by using a gated linear network. A top k% winner-take-all autoencoder is first built to generate a set of binary sequences as the gate control signals, so as to perform the input space partitioning. The piecewise linear model is then identified in an exact same way as a standard support vector regression (SVR) with a quasi-linear kernel composed by using the gate control signals. Results of our experiments shows that our proposed SVR prediction model outperforms other state-of-the-art methods.