An LSTM-based neural network method of particulate pollution forecast in China

Abstract

Particulate pollution has become more than an environmental problem in rapidly developing economies. Large-scale, long-term and high concentration of particulate pollution occurs much more frequently, which not only affects human health but also economic production. As PM10 is one of the main pollutants, the prediction of its concentration is of great significance. In this study, we present a PM10 forecast model based on the long short-term memory (LSTM) neural network method and evaluate its performance of predicting PM10 daily concentrations at five representative cities (Beijing, Taiyuan, Shanghai, Nanjing and Guangzhou) in China. Our model shows excellent adaptability for various regions in China. The predicted PM10 concentrations have good correlations with observations (R = 0.81-0.91). We also achieve great predication accuracy (70%-80%) on predicting the next-day changing trend and the model has the best performance for heavy pollution situation (PM10 > 100 μg m-3). In addition, the comparison of LSTM-based method and other statistical/machine learning methods indicates that our model is not only robust to different pollution intensities and geographic locations, but also with great potential on pollution forecast with temporal-correlated feature.