Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks
Date
2021
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Article
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Abstract
Air pollution has been shown to have a direct effect on human health. In particular, PM2.5 has been proven to be related to cardiovascular and respiratory problems. Therefore, it is important to have accurate models to predict high pollution events for this and other pollutants. We present different models that forecast PM2.5 maximum concentrations using a Long Short-Term Memory (LSTM) based neural network and a Deep Feedforward Neural Network (DFFNN). Ten years of air pollution and meteorological measurements from the network of monitoring stations in the city of Santiago, Chile were used, focusing on the behaviour of three zones of the city. All missing values were rebuilt using a method based on discrete cosine transforms and photochemical predictors selected through unsupervised clustering. Deep learning techniques provide significant improvements compared to a traditional multi-layer neural networks, particularly the LSTM model
configured with a 7-day memory window (synoptic scale of pollution patterns) can capture
critical pollution events at sites with both primary and secondary air pollution problems.
Furthermore, the LSTM model consistently outperform deterministic models currently used in Santiago, Chile.
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Citation
Menares, C., Perez, P., Parraguez, S., Fleming, Z.L.: Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks (2021), Urban Environment, 38, 100906, doi.org/10.1016/j.uclim.2021.100906
Keywords
Air quality forecasting, Meteorology forecast, Fine particulate matter, Deep neural networks, Machine learning, LSTM