A hybrid model for spatial–temporal prediction of PM2.5 based on a time division method

Liu, Bo; Wang, M.; Guesgen, H. W.

doi:10.1007/s13762-023-04813-z

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Combined with the hybrid model, which combines a residual neural network (ResNet), bidirectional long short-term memory (BiLSTM), and GCN, the adaptive monitoring network topology can adapt to the characteristics of pollutants and improve the prediction accuracy [52]. The GCN and CNN hybrid model, incorporating a spatiotemporal attention mechanism module, enhances the features of critical information collected from across the different dimensions [53]. Even though these models have performed well in AQP, they still struggle to handle non-stationary data issues.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network

Ding,

Noh

2023

Atmosphere

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To effectively address air pollution and enhance air quality, governments must be able to predict the air quality index with high accuracy and reliability. However, air quality prediction is subject to ambiguity and instability because of the atmosphere’s fluidity, making it challenging to identify the temporal and spatial correlations using a single model. Therefore, a new hybrid model is proposed based on an interpretable neural network and a graph neural network (INNGNN), which simulates the temporal and spatial dependence of air quality and achieves accurate multi-step air quality prediction. A time series is first interpreted using interpretable neural networks (INN) to extract the potentially important aspects that are easily overlooked in the data; second, a self-attention mechanism catches the local and global dependencies and associations in the time series. Lastly, a city map is created using a graph neural network (GNN) to determine the relationships between cities in order to extract the spatially dependent features. In the experimental evaluation, the results show that the INNGNN model performs better than comparable algorithms. Therefore, it is confirmed that the INNGNN model can effectively capture the temporal and spatial relationships and better predict air quality.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network

Ding,

Noh

2023

Atmosphere

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“…Згідно з рекомендаціями Всесвітньої організації здоров'я [1] вміст у пові- © Льовкін В.М., 2024 трі діоксиду азоту, який розглядається в цій роботі, протягом періоду в 24 години має становити 25 мкг/м 3 , але якщо рівень смертності, що відповідає цьому значенню, встановлювати рівним 100, то у випадку, якщо вміст за такий період досягне 50 мкг/м 3 , рівень смертності оцінюється у 102, а за досягнення 120 мкг/м 3…”

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Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting

Valerii

2024

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Air pollution forecasting is a well-known scientific problem, but nitrogen dioxide is not often considered as a pollutant in the problem. Traffic, presented by vehicle emissions, is one of the main sources of air pollution by nitrogen dioxide. Some existing investigations take traffic into account as input features for forecasting models but traffic is presented by values measured during previous hours. Therefore, separate models are created and trained for every air pollution measurement station. Trained forecasting models are not used systemati-cally and models for forecasting other indicators are not applied. But air pollution over cur-rent hour is caused by traffic mostly at the same place over the same hour but not over previ-ous hours. Therefore, it is important to investigate how usage of forecasted values of traffic impacts on air pollution by nitrogen dioxide in comparison with usage of traffic values meas-ured over previous hours. This task is considered in the paper. The objective of the study is to investigate impact of system approach on accuracy of forecasting air pollution by nitrogen dioxide. Three types of forecasting models based on long short-term memory were created to investigate such an impact. These types include models with concentration of nitrogen dioxide over previous hours as input features and two types of models with additional input features presented by values of traffic over previous hours and by forecasted values of traffic over output hours correspondingly. The last type of models pre-sented application of systems approach, as it uses other models available in the integrated forecasting environment. Experimental investigation allowed to create and train models as well as to compare the results of the models on the datasets representing car traffic and air pollution in Madrid. Application of the approach based on the principles of systems analysis increases accuracy of forecasting air pollution by nitrogen dioxide. It enables to create inte-grated air pollution forecasting environment, which combines information technologies of traffic and air pollution forecasting.

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A Hybrid Time Series Model for the Spatio-Temporal Analysis of Air Pollution Prediction Based on $$\textrm{PM}_{2.5}$$

Ahmad,

Kumar

2024

Communications in Computer and Information Science

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A hybrid model for spatial–temporal prediction of PM2.5 based on a time division method

Cited by 3 publications

References 38 publications

A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network

A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network

Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting

A Hybrid Time Series Model for the Spatio-Temporal Analysis of Air Pollution Prediction Based on $$\textrm{PM}_{2.5}$$

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