Abstract
Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.
Highlights
In the last decades, along with the rapid development of urbanization and industrialization, emissions of air pollutants, such as PM2.5, PM10, CO, and SO2, have caused serious environmental problems
Among various machine learning methods, artificial neural networks (ANNs) have been proved to have better prediction performance for air pollution compared with other models [26]
It contains 17,544 records of PM2.5 concentrations; 70% of its data are used as training samples while the remained 30% are used as testing samples
Summary
Along with the rapid development of urbanization and industrialization, emissions of air pollutants, such as PM2.5, PM10, CO, and SO2 , have caused serious environmental problems. Deep learning is one kind of advanced non-linear modeling techniques that was designed based on artificial neural networks but grows the neural-like calculation unit deeper for a better modeling It has been tested by several studies and was reported to have outstanding prediction performance in air quality forecasting. Li et al [17] proposed a long short-term memory neural network extended (LSTME) model that inherently considers spatial-temporal correlation for air pollutant concentration prediction. A special spatial-temporal filter (STF) layer is designed into the ordinary LSTM network to optimize the various spatial-temporal time series from the input layer In this way, highly correlated inputs are filtered, the influence of noisy data is mitigated, the complexity of the model is reduced, and the model performance can be improved.
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