Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression

Machine learning requires sufficient and reliable data to enhance the prediction performance. However, environmental data sometimes is short and/or contains missing data. Often existing prediction models built on machine learning fail to predict environmental problems accurately. We argue that spatial domain data can be used to facilitate the training of temporal prediction model. This paper formulates mathematically a spatial data aided incremental support vector regression (SalncSVR) for spatio-temporal PM_2.5 prediction. We conduct spatio-temporal PM_2.5 prediction over 13 monitoring stations in Auckland New Zealand, and compare the proposed SalncSVR with a pure temporal IncSVR prediction.