Highly Efficient Picture-Based Prediction of PM2.5 Concentration

Gu, Ke; Qiao, Junfei; Li, Xiaoli

doi:10.1109/tie.2018.2840515

Cited by 106 publications

(67 citation statements)

References 37 publications

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“…The last category corresponds to a new Picture-based Predictor of PM2.5 Concentration (PPPC) [48] that generates real-time estimates of PM2.5 concentrations using images captured by cell phones or cameras. This model possesses great advantages in terms of prediction accuracy and implementation efficiency.…”

Section: The Methods Considered For Comparisonmentioning

confidence: 99%

PM2.5 Estimation Based on Image Analysis

Li¹,

Shan²,

Wang³

2020

KSII TIIS

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For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

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Section: The Methods Considered For Comparisonmentioning

confidence: 99%

PM2.5 Estimation Based on Image Analysis

Li¹,

Shan²,

Wang³

2020

KSII TIIS

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“…As the PM 2.5 concentration elevates, the image colorfulness will decrease [12]. Images with high image colorfulness usually show more details and relative information.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Among all of the typical air pollutants, PM 2.5 with the aerodynamic diameters of 2.5 m or less has attracted a lot of attentions. Furthermore, PM 2.5 is able to easily transmit hazardous chemicals into the human body and thus causes serious diseases by damaging cerebrovascular, cardiovascular and respiratory functions [12]. Hence, the majority of residents gradually realize the significance of predicting PM 2.5 concentration and taking prophylactic measures before the arrival of severe smoggy conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Qiao

2020

SN Appl. Sci.

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The fine particulate matter (PM 2.5) problem has become one of the biggest topics of concern in many cities, such as Beijing in China. This study attempts to employ a self-organizing fuzzy neural network (SOFNN) to predict the hourly concentration of PM 2.5 using multi-source data. The multi-source data including meteorological data, pollutant concentration data and image data are first obtained through different methods. Then, a novel SOFNN, which is optimized by an improved gradient descent algorithm, is proposed based on the mutual information (MI) and sensitivity analysis (SA). More importantly, MI and SA are introduced to effectively adjust the structure of fuzzy neural network without changing the performance of the original network. Finally, the MI-and SA-based SOFNN is deployed to infer PM 2.5 concentration at the study area. The experimental results show that our proposed model can achieve a more compact network structure, higher prediction performance and lower computation time as compared with state-of-the-art or popular methods.

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“…However, the insufficient combustion of flare gas is the top problem faced by petrochemical enterprises, since it will produce much flare soot [1], which will then destroy the atmospheric ecological balance and harm human health [2]- [3]. For example, the sulfur dioxide and nitrogen dioxide in flare soot will react with water on rainy days to form acid rain, which will destroy soil properties; and the harm caused by the fine particulate matter (PM2.5) in flare soot to the human body is selfevident [4]- [6]. Therefore, the full combustion of flare gas is a powerful measure to reduce the emission of flare soot.…”

Section: Introductionmentioning

confidence: 99%

Upsampling Flare Soot Pictures by Fusing Global Perception and Local Details

Zhang

Wang

et al. 2020

IEEE Access

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The flare stack is a flare gas combustion facility used to ensure the safe production of petrochemical enterprises. However, in petrochemical enterprises, cameras are far away from the flare stacks, which causes the collected flare soot pictures to be too blurry to be analyzed further. To that end, we devise an Upsampling Flare Soot Pictures Director (UFSPD) based on the global perception and local details. First, we get the global perception map and the local details map of a low resolution flare soot picture via guided filtering and edge extraction, respectively. Then, the upsampling method is used to upsample the above two maps to get the corresponding high-resolution picture. Finally, the two pictures in the second step are fused at the pixel-level to get a satisfactory flare soot picture. Experiments show that the proposed UFSPD model is more competitive than other state-of-the-art models in some image quality evaluation indexes, and it also helps to improve the detection rate of flare soot.

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Highly Efficient Picture-Based Prediction of PM2.5 Concentration

Cited by 106 publications

References 37 publications

PM2.5 Estimation Based on Image Analysis

PM2.5 Estimation Based on Image Analysis

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Upsampling Flare Soot Pictures by Fusing Global Perception and Local Details

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