Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method

Wu, Cui-Lin; He, Hong-di; Song, Ruifeng; Peng, Zhong-Ren

doi:10.1016/j.buildenv.2021.108436

Cited by 25 publications

(5 citation statements)

References 42 publications

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“…Those results suggest that images may capture similar features as traditional GIS predictors while also allowing for the inclusion of higher-resolution (i.e., street-level) features that traditional GIS variables lack . Another notable trend in recent air quality modeling studies is the adoption of advanced machine learning approaches to improve model performance, including random forest, − gradient boosting, , artificial neural network, − and hybrid algorithms. , …”

Section: Introductionmentioning

confidence: 96%

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

Dixit

Marshall

et al. 2022

Environ. Sci. Technol.

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Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO 2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO 2 . Our results suggest that street view imagery alone may provide sufficient information to explain NO 2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10fold cross-validation R 2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)�a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intraurban variation of NO 2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

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Section: Introductionmentioning

confidence: 96%

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

Dixit

Marshall

et al. 2022

Environ. Sci. Technol.

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“…Among them, a recurrent neural network (RNN) [34] has been used to capture temporal dependence. RNN's enhanced models-long short-term memory (LSTM) [35][36][37] and gated recurrent units (GRUs) [38,39]-provide an improved long-term dependency model for AQP. Some researchers have proposed improved LSTM-related models, including read-first LSTM (RLSTM) [40] and vanilla LSTM with multichannel input and multi-route output (IVLSTM-MCMR) [41], which have enhanced the function of the multidimensional feature extraction of air quality.…”

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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“…In addition to observing the impacts of the COVID-19 epidemic on the air, ref. [35] forecasted the PM 10 and SO 2 air contaminants in Sakarya. For the prediction, they employed "recurrent artificial neural networks".…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

Sarwar,

Aziz,

Balsalobre-Lorente

2023

Sustainability

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Currently, the world is facing the problem of climate change and other environmental issues due to higher emissions of greenhouse gases. Saudi Arabia is not an exception due to the dependence of the Saudi economy on fossil fuels, which adds to the problem. However, due to the nonlinear pattern of pollution-creating gases, including nitrogen and sulfur dioxide, it is not effortless to rely on forecasting accuracy. Nevertheless, it is essential to denoise the data to extract the reliable outcomes used by different econometric approaches. Hence, the current paper introduces a hybrid model combining compressed sensor denoising (CSD) with traditional regression, machine learning, and deep learning techniques. Comparing different hybrid models and various denoising techniques revealed that CSD-GAN is the best model for accurately predicting NO2 and SO2, as compared with ARIMA, RLS, and SVR. Also, when the comparison is made between predicted and actual NO2 and SO2 levels, these are aligned, proving that CSD-GAN is superior in its level and direction of prediction. It can be concluded that the GAN model is the best hybrid model for predicting NO2 and SO2 emissions in Saudi Arabia. Hence, this model is recommended to policymakers for predicting environmental externalities and framing policies accordingly.

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Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method

Cited by 25 publications

References 42 publications

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

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

Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

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Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method

Cited by 25 publications

References 42 publications

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

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

Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

Contact Info

Product

Resources

About

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations