A Prediction of PM2.5 Concentration Based on Temporal-Spatial Fusion Model

Abstract

In this paper, a temporal-spatial fusion model is proposed for PM2.5 concentration prediction. The model uses historical PM2.5 concentration and meteorological data as input of the model to make hourly predictions of PM2.5 concentration. This model consists of three parts: 1) Long short-term memory neural network predictor based on time dimension, 2) Artificial neural network predictor based on spatial dimension, 3) Model tree predictor based on temporal-spatial fusion. This method combines the forecast results of two dimensions in space and time dynamically, as the spatial and temporal correlation of data is considered. Experimental results show this model performs better than predicting from a single dimension, confirming the effectiveness of the model.