Analysis and Prediction of Air Quality based on LSTM Neural Network —— Take Beijing Temple of Heaven as an Example

A neural network model based on Long Short-Term Memory (LSTM) demonstrates high accuracy and robustness in predicting air quality at Beijing's Temple of Heaven. The study analyzed meteorological data from 2016 to 2018, revealing strong correlations between weather elements and air pollution levels. By leveraging LSTM's ability to process time-series data effectively, researchers developed a predictive model that outperforms traditional approaches. The findings provide actionable insights for implementing targeted air pollution control measures, offering a scientific framework to mitigate environmental risks at culturally significant sites.

Fang, H., & Zhang, L. (2020). Analysis and Prediction of Air Quality based on LSTM Neural Network —— Take Beijing Temple of Heaven as an Example. Journal of Physics: Conference Series, 1637(1), 12126. https://doi.org/10.1088/1742-6596/1637/1/012126