An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

Guo, Canyang; Liu, Genggeng; Lyu, Lingjuan; Chen, Chi‐Hua

doi:10.1109/access.2020.3032420

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…An unsupervised model is proposed by Guo et al. ( 2020 ) to predict PM 2.5 in Hubei, China. The model corresponds to a hybrid of a GRU and an inverse distance weighted KNN.…”

Section: Classification By Used Model Of the Contributions On Air Qua...mentioning

confidence: 99%

Machine learning algorithms to forecast air quality: a survey

2023

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Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

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“…An unsupervised model is proposed by Guo et al. ( 2020 ) to predict PM 2.5 in Hubei, China. The model corresponds to a hybrid of a GRU and an inverse distance weighted KNN.…”

Section: Classification By Used Model Of the Contributions On Air Qua...mentioning

confidence: 99%

Machine learning algorithms to forecast air quality: a survey

2023

View full text Add to dashboard Cite

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“…e back propagation (BP) neural network [38][39][40][41][42][43][44][45] is based on intelligent machine learning, has nonlinear features, good classification ability, and mapping ability to multidimensional functions, and has great advantages in multivariate regression. It has an input layer, an intermediate layer, and an output layer.…”

Section: Model Designmentioning

confidence: 99%

Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis

Guo

Lou

Wang

et al. 2021

Journal of Advanced Transportation

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Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment.

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“…Among them, MODIS aerosol products are widely used. Recent remote sensing research has extensively explored the PM 2.5 estimation using satellite AOD as the main data source [11][12][13]. Therefore, an accurate and representative AOD average product is critical for such analyses.…”

Section: Introductionmentioning

confidence: 99%

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

Wang,

Zhang,

Zhou

et al. 2023

Remote Sensing

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Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively.

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An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

Cited by 10 publications

References 22 publications

Machine learning algorithms to forecast air quality: a survey

Machine learning algorithms to forecast air quality: a survey

Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

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