Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

Li, Songzhou; Xie, Gang; Ren, Jinchang; Guo, Lei; Yang, Yunyun; Xu, Xin

doi:10.3390/app10061953

Cited by 91 publications

(42 citation statements)

References 26 publications

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“…The first one (i.e., AE-BiLSTM) is a combination of auto-encoder and bi-LSTM neural network [21]. The second one, AC-LSTM proposed in [22], lever-…”

Section: A Evaluation Settingsmentioning

confidence: 99%

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PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model

Nguyen

et al. 2021

IEEE Access

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The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 prediction, which can potentially contribute to reducing or avoiding the negative consequences. Our approach's novelty is to utilize the genetic algorithm (GA) and an encoder-decoder (E-D) model for PM2.5 prediction. The GA benefits feature selection and remove outliers to enhance the prediction accuracy. The encoder-decoder model with long short-term memory (LSTM), which relaxes the restrictions between the input and output of the model, can be used to effectively predict the PM2.5 concentration. We evaluate the proposed model on air quality datasets from Hanoi and Taiwan. The evaluation results show that our model achieves excellent performance. By merely using the E-D model, we can obtain more accurate (up to 53.7%) predictions than those of previous works. Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. By combining the GA-based feature selection algorithm and the E-D model, our proposed approach further improves the accuracy by at least 13.7%.

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“…The first one (i.e., AE-BiLSTM) is a combination of auto-encoder and bi-LSTM neural network [21]. The second one, AC-LSTM proposed in [22], lever-…”

Section: A Evaluation Settingsmentioning

confidence: 99%

“…This section compares our ED-LSTM to AE-BiLSTM [21], AC-LSTM [22], and ST-DNN [11]. Since all three models' source codes are not publicly available, we have initially reimplemented them.…”

Section: Comparing Prediction Modelsmentioning

confidence: 99%

PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model

Nguyen

et al. 2021

IEEE Access

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“…e attention mechanism was originally used in machine translation [34], but it is now an important part of neural network structures, and it is also widely used in image processing, speech recognition, and computer-related fields [35]. In the recent literature, Li et al [36] proposed to use the attention mechanism to capture the most important part of the past state, but ignored the relative importance of neighboring sites. In addition, non-time series (road network and POI) also affects the prediction of the target area.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Zou

Zhao

Duan

et al. 2021

Mobile Information Systems

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With the rapid development of the Internet of Things and Big Data, smart cities have received increasing attention. Predicting air quality accurately and efficiently is an important part of building a smart city. However, air quality prediction is very challenging because it is affected by many complex factors, such as dynamic spatial correlation between air quality detection sensors, dynamic temporal correlation, and external factors (such as road networks and points of interest). Therefore, this paper proposes a long short-term memory (LSTM) air quality prediction model based on a spatiotemporal attention mechanism (STA-LSTM). The model uses an encoder-decoder structure to model spatiotemporal features. A spatial attention mechanism is introduced in the encoder to capture the relative influence of surrounding sites on the prediction area. A temporal attention mechanism is introduced in the decoder to capture the time dependence of air quality. In addition, for spatial data such as point of interest (POI) and road networks, this paper uses the LINE graph embedding method to obtain a low-dimensional vector representation of spatial data to obtain abundant spatial features. This paper evaluates STA-LSTM on the Beijing dataset, and the root mean square error (RMSE) and R-squared ( R 2 ) indicators are used to compare with six benchmarks. The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.

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“…6 have applied three machine learning models that forecasted PM2.5 concentrations and their results showed that the variability was 80% (R 2 = 0.8) in the concentrations of PM2.5 and 75% of the pollution levels were predicted. 7 made an attention mechanism to capture the degree of signi cance of the effects on future concentrations of PM2.5 of the featured states at different times in the past. 8 have studied the PM2.5 using Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN).…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Predicting Particulate Matter (PM2.5) Levels Using LSTM Models

Balamurali¹,

Partheeban²,

Elamparithi³

et al. 2021

Preprint

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In recent times, air pollution has attracted the attention of policymakers and researchers as an important issue. The pollution that contaminates the air that people breathe is from pollutants such as oxides of carbon, nitrogen and sulphur as well minuscule dust particle which are smaller than 0.0025mm in diameter. The emissions contain many substances that are harmful to human health when exposed to them for a prolonged period or more than certain levels of concentration. The recent advent of technology in sensors and compact instruments to measure the concentration of pollutant levels with considerable ease. Further, this paper also predicts the air pollution for using multiple Deep Learning models that are variations of the Long Short-Term Memory (LSTM) model. In this research, only PM2.5 alone taken into consideration for prediction. Real-time air quality data were collected at selected places in the study area. It is found that the model prediction data is well matched with the other researchers' results and real-time data.

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Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

Cited by 91 publications

References 26 publications

PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model

PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Comparative Study on Predicting Particulate Matter (PM2.5) Levels Using LSTM Models

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