Area of research
Geochemistry and Petrology · Artificial Intelligence
Research interest
Research topics from publications: Spatiotemporal prediction of air quality based on LSTM neural network. Representative work: Accurate monitoring of air quality is of great importance to our daily life. By predicting the air quality in advance, we can make timely warnings and defenses to minimize the threat to life. With a large number of environmental data, the air quality prediction based on deep learning technology is studied in depth. Based on long short-term memory (LSTM), a comprehensive prediction model with multi-output and multi-index of supervised learning (MMSL) was proposed. The particle concentration data (mainly PM2.5, means particles with aerodynamic diameter ≤ 2.5 mm) of the present monitoring station, as well as that of the nearest neighbor stations, the meteorological data, and the gaseous polluta