A hybrid integrated deep learning model for predicting various air pollutants

Mao, Wenjing; Jiao, Limin; Wang, Weilin; Wang, Jianlong; Tong, Xueli; Zhao, Shenghui

doi:10.1080/15481603.2021.1988429

Cited by 21 publications

(9 citation statements)

References 52 publications

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“…al. ( 2021a ) PM 2.5 , PM 10, NO 2 , SO 2 , O 3 and CO Graph convolutional temporal sliding-LSTM Air pollution data from air quality monitoring station BTH area, China, and meteorological data from China Meteorological Data Service Center Harishkumar et. al.…”

Section: Resultsmentioning

confidence: 99%

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Nandi

Singh

Jain

et al. 2023

Int. J. Environ. Sci. Technol.

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The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

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Section: Resultsmentioning

confidence: 99%

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Nandi

Singh

Jain

et al. 2023

Int. J. Environ. Sci. Technol.

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“…The findings showed that the suggested technique beats existing algorithms in terms of performance. Mao et al used graph convolution and LSTM networks to create and present a spatiotemporal modeling hybrid deep learning framework to forecast various air contaminants (Mao et al, 2021 ). Models such as MLR and LSTM networks were employed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Factories' smoke exhaust, pollution caused by vehicles' exhaust, and power plants are the primary causes of air quality degradation (Sultana, 2019 ). (PM2.5, PM10), O3, SO2, CO, and NO2 are the five categories of air pollutants (Mao et al, 2021 ). PM2.5 is the most concerning air pollution component because these particles are small and light.…”

Section: Introductionmentioning

confidence: 99%

Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques

Morapedi,

Obagbuwa

2023

Front. Artif. Intell.

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BackgroundAir pollution contributes to the most severe environmental and health problems due to industrial emissions and atmosphere contamination, produced by climate and traffic factors, fossil fuel combustion, and industrial characteristics. Because this is a global issue, several nations have established control of air pollution stations in various cities to monitor pollutants like Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Particulate Matter (PM2.5, PM10), to notify inhabitants when pollution levels surpass the quality threshold. With the rise in air pollution, it is necessary to construct models to capture data on air pollutant concentrations. Compared to other parts of the world, Africa has a scarcity of reliable air quality sensors for monitoring and predicting Particulate Matter (PM2.5). This demonstrates the possibility of extending research in air pollution control.MethodsMachine learning techniques were utilized in this study to identify air pollution in terms of time, cost, and efficiency so that different scenarios and systems may select the optimal way for their needs. To assess and forecast the behavior of Particulate Matter (PM2.5), this study presented a Machine Learning approach that includes Cat Boost Regressor, Extreme Gradient Boosting Regressor, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Decision Tree.ResultsCat Boost Regressor and Extreme Gradient Boosting Regressor were implemented to predict the latest PM2.5 concentrations for South African Cities with recording stations using past dated recordings, then the best performing model between the two is used to predict PM2.5 concentrations for South African Cities with no recording stations and also to predict future PM2.5 concentrations for South African Cities. K-Nearest Neighbor, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest Classifier were implemented to create a system predicting the Air Quality Index (AQI) Status.ConclusionThis study investigated various machine learning techniques for air pollution to analyze and predict air pollution behavior regarding air quality and air pollutants, detecting which areas are most affected in South African cities.

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“…The concentration of atmospheric pollutants varies across different regions and at different time points. 15,16 On one hand, pollutants can spill over between different areas, 17,18 and on the other hand, they can be absorbed by trees and other vegetation within a region. 19,20 Therefore, understanding the disparities of pollutant concentrations becomes highly challenging.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The concentration of atmospheric pollutants varies across different regions and at different time points. , On one hand, pollutants can spill over between different areas, , and on the other hand, they can be absorbed by trees and other vegetation within a region. , Therefore, understanding the disparities of pollutant concentrations becomes highly challenging. Traditional methods for predicting pollutants primarily rely on multivariate linear regression (MLR) , approaching the data from a macro perspective and forecasting the overall concentration of pollutants for an entire region.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

Xia,

McCracken,

Liu

et al. 2024

Environ. Sci. Technol.

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In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

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A hybrid integrated deep learning model for predicting various air pollutants

Cited by 21 publications

References 52 publications

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

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