A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network
2021
Computational Intelligence and Neuroscience
Accurate monitoring of air quality can no longer meet people's needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data
doi:10.1155/2021/1616806
pmid:34712315
pmcid:PMC8548155
fatcat:pzfhhqjbc5gklfit42l4hngwxa