A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory

Qi, Yanlin; Li, Qi; Karimian, Hamed; Liu, Di

doi:10.1016/j.scitotenv.2019.01.333

Cited by 455 publications

(184 citation statements)

References 24 publications

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“…Taghvaee et al [12] reported that diesel exhaust and industrial emissions have a greater impact on cancer risks (~70%) than other air pollution sources in Tehran. Tehran is not the city in Iran with the worst air pollution, however, it has received more attention [8,[12][13][14][15][16][17][18] because of its large population (estimated to be 9 million in 2019 [19]). Dehghan et al [18] investigated the impact of Tehran's air pollution on the mortality rate related to respiratory diseases.…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

et al. 2019

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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

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Section: Introductionmentioning

confidence: 99%

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

et al. 2019

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“…Hoek et al [19] concluded that land-use regression methods are able to model annual mean PM 2.5 concentrations. The LUR model is considered to be suitable for PM2.5 prediction due to the linear relationship between PM2.5 and explanatory variables, while the ANN based model designed to handle non-linearity may perform better in general as well [20]. Kunwar et al [21] applied an ensemble learning method and a principal components analysis (PCA) algorithm to integrate air quality data to forecast air quality index (AQI) values.…”

Section: Related Researchmentioning

confidence: 99%

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020

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Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

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“…In recent years, LSTM algorithms have achieved good research results in the field of simulation and prediction of the evolution process of air pollutant particle concentration, and the representative algorithms include: LSTM method and evaluation algorithm [18], ensemble-LSTM algorithm [19], CNN-LSTM algorithm [20], LSTM-FC algorithm [21]; LSTM algorithms based on air pollutant particle concentration characteristics: GC-LSTM algorithm [22], spatiotemporal convolutional LSTM algorithm [23]; LSTM algorithm based on deep learning: DL-LSTM algorithm [24], Multi-output DL-LSTM algorithm [25]; Deep DL-LSTM algorithm [26]. Algorithms of this type took the LSTM algorithm as the core, starting from the structural characteristics of the research object, they improved the LSTM algorithm to effectively simulate the evolution process of the concentration of atmospheric pollutant particles and improve the prediction performance of the algorithm, moreover, based on data analysis, they introduced CNN, FC, GC, DL, and other algorithms to optimize the input data and preliminarily explored the spatial correlation of the evolution process of particle concentration.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Correlation-Based LSTM Algorithm and Its Application in PM2.5 Prediction

Zhao¹

2020

RIA

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In existing researches, the algorithms for simulating and predicting the evolution process of air pollutant particle concentration have neither explored the spatial correlation of particle concentration in depth, nor achieved the fusion of the time dependence and the spatial correlation of the particle concentration. To this end, this paper proposes the longshort term memory network (LSTM) algorithm based on spatiotemporal fusion. First, the spatial correlation, the relevant factors and the calculation methods are proposed; then, the local spatial correlation factors are combined with the forget gate and the remember gate of the LSTM algorithm to construct a LSTM algorithm based on local spatial information and spatial-temporal correlation, namely the LTS-LSTM; after that, the learning result of LTS-LSTM is combined with the global spatial correlation factors to construct a LSTM algorithm based on global spatial information and spatial-temporal correlation, namely the GTS-LSTM; at last, the proposed algorithm is adopted to simulate the global and local air pollution particle concentration evolution process, and predict the particle concentration. On the global and local observation dataset, the proposed algorithm is compared with the regression algorithm, support vector machine (SVM), fuzzy neural network (FNN), LSTM neural network, GC-LSTM neural network, and DL-LSTM neural network. The comparison results show that: in terms of air particle concentration prediction, the performance of the proposed algorithm outperforms other traditional prediction algorithms, and its performance is close to the deep LSTM algorithms.

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A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory

Cited by 455 publications

References 24 publications

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Spatial-Temporal Correlation-Based LSTM Algorithm and Its Application in PM2.5 Prediction

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