Development of a PM2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea

Ho, Chin Siong; Park, Ingyu; Oh, Han Jin; Gim, Hyeon-Ju; Hur, Sun-Kyong; Kim, Jinwon; Choi, Dae-Ryun

doi:10.1016/j.atmosenv.2020.118021

Cited by 49 publications

(21 citation statements)

References 28 publications

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“…Artificial NNs, a branch of machine learning, have been proved to be promising for time-dependent forecast, for example, RNN [20], LSTM [21], and GRU [22]. An artificial NNs approach is to construct a model by simulating human's NNs.…”

Section: Statistics-based Methodsmentioning

confidence: 99%

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Zhang

Jin

Zou

et al. 2021

Mathematical Problems in Engineering

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Air pollution has become a critical issue in human’s life. Predicting the changing trends of air pollutants would be of great help for public health and natural environments. Current methods focus on the prediction accuracy and retain the forecasting time span within 12 hours. Shorter time span decreases the practicability of these perditions, even with higher accuracy. This study proposes an attention and autoencoder (A&A) hybrid learning approach to obtain a longer period of air pollution changing trends while holding the same high accuracy. Since pollutant concentration forecast highly relates to time changing, quite different from normal prediction problems like autotranslation, we integrate “time decay factor” into the traditional attention mechanism. The time decay factor can alleviate the impact of the value observed from a longer time before while increasing the impact of the value from a closer time point. We also utilize the hidden states in the decoder to build connection between history values and current ones. Thus, the proposed model can extract the changing trend of a longer history time span while coping with abrupt changes within a shorter time span. A set of experiments demonstrate that the A&A learning approach can obtain the changing trend of air pollutants, like PM2.5, during a longer time span of 12, 24, or even 48 hours. The approach is also tested under different pollutant concentrations and different periods and the results validate its robustness and generality.

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Section: Statistics-based Methodsmentioning

confidence: 99%

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Zhang

Jin

Zou

et al. 2021

Mathematical Problems in Engineering

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“…Additionally, geographical conditions and human activities also affect the distribution of PM 2.5 concentrations. Many studies [10,11,14,34,37] have also incorporated synoptic conditions, geographical conditions, human activities, and season effects into the corresponding models to improve the models' performance. Our study assumed that the combination of the internal historical change trend derived from the air quality monitoring stations and the dynamic disturbance of the synoptic conditions, geographical conditions, human activities, and seasonal effect together affect future PM 2.5 concentrations.…”

Section: Part 3: Auxiliary Data Incorporationmentioning

confidence: 99%

“…Existing studies show that machine learning-and deep learning-based models have been widely applied in atmospheric environmental modeling for the monitoring and prediction of air pollutants, such as the multilayer perceptron (MLP) model [26,27], the backpropagation neural network (BPNN) model [28], support vector regression (SVR) [28,29], the random forest (RF) model [30][31][32], the general regression neural network (GRNN) model [28,33], the recurrent neural network (RNN) model [34], and long short-term memory (LSTM)-based models [11,[35][36][37][38]. The careful reasoning process in machine learningbased models (such as MLP, SVR, and RF) is comparable to mathematical reasoning [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

et al. 2021

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Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

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“…Over the years, several approaches have been developed to predict and manage PM. Chang-Hoi et al [6] utilized RNN incorporated with CMAQ (Community Multiscale Air Quality) to forecast PM2.5. Ting Tsai et al [7] employed the RNN model to predict PM2.5 concentrations, but the result of errors such as RMSE and MAE are high.…”

Section: Introductionmentioning

confidence: 99%

oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model

2022

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High particulate matter (PM) concentrations in the cleanroom semiconductor factory have become a significant concern as they can damage electronic devices during the manufacturing process. PM can be predicted before becoming more concentrated based on its historical data to support factory management in regulating the air quality in the cleanroom. In this paper, a Multi-Dense Layer BiLSTM model is proposed to predict PM2.5 concentrations in the indoor environment of the cleanroom. To obtain reliability, validity, and interoperability data, the datasets containing temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 were retrieved in a standardized manner via oneM2M-defined representational state transfer application programmable interfaces by employing software platforms compliant with the Internet of Things (IoT) standard. Based on the proposed model, an algorithm was built providing short-term PM2.5 concentration predictions (one hour ahead, two hours ahead, and three hours ahead). The proposed model outperformed the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM models in terms of MSE, MAE, and MAPE values. The model created in this study could predict high PM2.5 concentration levels more accurately, thus providing vital support for operation and maintenance for the semiconductor industry.

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Development of a PM2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea

Cited by 49 publications

References 28 publications

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model

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