Air quality prediction using spatio-temporal deep learning

Hu, Keyong; Guo, Xiaolan; Gong, Xueyao; Liang, Jing; Li, Daoquan

doi:10.1016/j.apr.2022.101543

Cited by 23 publications

(10 citation statements)

References 18 publications

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“…Furthermore, Hu et al. [ 63 ] inset a one-dimensional layer to capture both local and global dependencies, while Wang et al. [ 64 ] used convolutional and recurrent layers.…”

Section: Methods Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Deep-learning architecture for PM2.5 concentration prediction: A review

Zhou,

Wang,

Zhu

et al. 2024

Environmental Science and Ecotechnology

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“…Furthermore, Hu et al. [ 63 ] inset a one-dimensional layer to capture both local and global dependencies, while Wang et al. [ 64 ] used convolutional and recurrent layers.…”

Section: Methods Reviewmentioning

confidence: 99%

“…[ 71 ] 2022 Jing-Jin-Ji Region, China MGC-LSTM H/S/T+1 2.91 2.16 12.96 - Hu et al. [ 63 ] 2022 Beijing, China Conv1D-LSTM H/S/T+1 20.76 11.20 - 0.96 Wu et al. [ 69 ] 2022 Beijing, China CE-AGA-LSTM H/S/T+1 21.88 14.49 - 0.95 Waseem et al.…”

Section: Methods Reviewmentioning

confidence: 99%

Deep-learning architecture for PM2.5 concentration prediction: A review

Zhou,

Wang,

Zhu

et al. 2024

Environmental Science and Ecotechnology

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“…Launched in 2014, it has the incomparable advantages of 10 min time resolution and 500 m spatial resolution simultaneously, which ensure abundant reliable data are detected and used in distinguished meteorological models. Hourly AOD data could be derived from Himawari-8 and subsequently ensure multiple relevant research studies in China [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China

Zou,

Huang,

et al. 2024

Remote Sensing

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As the urgency of PM2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network (TCN), were found to be efficient in atmospheric pollutant estimation, but either the model accuracy was not high enough or the models encountered certain challenges due to their own structure or some specific application scenarios. This study proposed a high-accuracy, hourly PM2.5 forecasting model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by deep-learning methods, based on air pollutant data and meteorological data, and aerosol optical depth (AOD) data retrieved from the Himawari-8 satellite. This research was based on the Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020–2022. The PD-LL-Transformer had three parts: a poly-dimensional embedding layer, which integrated the advantages of allocating and embedding multi-variate features in a more refined manner and combined the superiority of different temporal processing methods; a local-LSTM block, which combined the advantages of LSTM and TCN; and a Transformer encoder block. Over the test set (the whole year of 2022), the model’s R2 was 0.8929, mean absolute error (MAE) was 4.4523 µg/m3, and root mean squared error (RMSE) was 7.2683 µg/m3, showing great accuracy for PM2.5 prediction. The model surpassed other existing models upon the same tasks and similar datasets, with the help of which a PM2.5 forecasting tool with better performance and applicability could be established.

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“…Applications of AI towards predicting PM concentrations can be found in the literature, with the first work of this kind published almost two decades ago [24]. Over last few years, the community has been actively exploring Deep Learning approaches to PM prediction, with very good results [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Despite the accurate predictions of ANNs, and the fact that they often outperform classical machine learning algorithms, they receive criticism for being "black boxes" [43].…”

Section: Introductionmentioning

confidence: 99%

A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City

Anagnostopoulos,

Rigas,

Papachristou

et al. 2023

Atmosphere

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Particulate matter (PM) pollution is a major global concern due to its negative impact on human health. To effectively address this issue, it is crucial to have a reliable and efficient forecasting system. In this study, we propose a framework for predicting particulate matter concentrations by utilizing publicly available data from low-cost sensors and deep learning. We model the temporal variability through a novel Long Short-Term Memory Neural Network that offers a level of interpretability. The spatial dependence of particulate matter pollution in urban areas is modeled by incorporating characteristics of the urban agglomeration, namely, mean population density and mean floor area ratio. Our approach is general and scalable, as it can be applied to any type of sensor. Moreover, our framework allows for portable sensors, either mounted on vehicles or used by people. We demonstrate its effectiveness through a case study in Greece, where dense urban environments combined with low cost sensor networks is a peculiarity. Specifically, we consider Patras, a Greek port city, where the net PM pollution comes from a variety of sources, including traffic, port activity and domestic heating. Our model achieves a forecasting accuracy comparable to the resolution of the sensors and provides meaningful insights into the results.

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Air quality prediction using spatio-temporal deep learning

Cited by 23 publications

References 18 publications

Deep-learning architecture for PM2.5 concentration prediction: A review

Deep-learning architecture for PM2.5 concentration prediction: A review

PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China

A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City

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