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 PM _10 is one of the main pollutants, the prediction of its concentration is of great significance. In this study, we present a PM _10 forecast model based on the long short-term memory (LSTM) neural network method and evaluate its performance of predicting PM _10 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 PM _10 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 (PM _10 > 100 μ g 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.

Keywords

• LSTM
• PM&lt, sub&gt, 10&lt, /sub&gt, concentration
• neural network
• forecast model