Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

Zheng, Tongshu; Bergin, Michael H.; Hu, Shijia; Miller, Joshua; Carlson, David

doi:10.1016/j.atmosenv.2020.117451

Cited by 51 publications

(45 citation statements)

References 39 publications

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“…Therefore, the following conclusions can be drawn: (1) The method of time dynamic decomposition is suitable for the extraction of neighbor structural information representing the structural characteristics of time series. (2) The hybrid prediction model integrates the neighbor structural information to make up for the lack of structural characteristics of time series when machine learning models perform time series prediction. (3) The neighbor structural information extraction algorithm based on dynamic decomposition is generally applicable to traditional machine learning models.…”

Section: Discussionmentioning

confidence: 99%

“…In 2015, PM 2.5 was considered the fifth leading risk factor of death, which has caused 4.2 million deaths worldwide [1,2]. Being an essential index to describe the quality of atmospheric environment, the higher the PM 2.5 concentration is, the more serious the air pollution is [3].…”

Section: Introductionmentioning

confidence: 99%

“…Suleiman et al [18] analyzed the emission reduction effect of traffic-related PM 10 and PM 2.5 of 19 stations in London by using the evaluation system built by ANN, BRT (boosted regression tree) and SVM. Zheng et al [2] extracted the dynamic variation characteristics of daily satellite images based on the convolutional neural network, and then applied random forest regression to achieve ground-level PM 2.5 estimation. Zhou et al [23] integrated copula function into a hybrid model composed of multiple deterministic ANN and Bayesian models, effectively eliminating data conversion and error correction process in order to obtain accurate ensemble probability prediction of PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

Wang

Feng

et al. 2021

Sustainability

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PM2.5 concentration prediction is an important task in atmospheric environment research, so many prediction models have been established, such as machine learning algorithm, which shows remarkable generalization ability. The time series data composed of PM2.5 concentration have the implied structural characteristics such as the sequence characteristic in time dimension and the high dimension characteristic in dynamic-mode space, which makes it different from other research data. However, when the machine learning algorithm is applied to the PM2.5 time series prediction, due to the principle of input data composition, the above structural characteristics can not be fully reflected. In our study, a neighbor structural information extraction algorithm based on dynamic decomposition is proposed to represent the structural characteristics of time series, and a new hybrid prediction system is established by using the extracted neighbor structural information to improve the accuracy of PM2.5 concentration prediction. During the process of extracting neighbor structural information, the original PM2.5 concentration series is decomposed into finite dynamic modes according to the neighborhood data, which reflects the time series structural characteristics. The hybrid model integrates the neighbor structural information in the form of input vector, which ensures the applicability of the neighbor structural information and retains the composition form the original prediction system. The experimental results of six cities show that the hybrid prediction systems integrating neighbor structural information are significantly superior to the traditional models, and also confirm that the neighbor structural information extraction algorithm can capture effective time series structural information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

Wang

Feng

et al. 2021

Sustainability

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“…The technique has been widely implemented in the field of computer vision, such as facial recognition [19], image classification [16], [17], [18] and visual tracking [20]. Zheng [21] and Hong [22] used CNN models to analyze satellite images and estimate ground-level PM 2.5 values. Apart from CNN, Hochreiter [23] proposed an LSTM model to extract features from sequential data for neural machine translation [24], [25].…”

Section: Related Workmentioning

confidence: 99%

ResNet-LSTM for Real-Time PM_2.5 and PM₁₀ Estimation Using Sequential Smartphone Images

et al. 2020

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“…Bai et al [30] compared and analyzed the difference in PM 2.5 estimation accuracies based on TOA and AOD through four models, random forest, background gradient lifting regression, xgboost, and support vector regression (SVR), and they verified the reliability of PM 2.5 estimation based on TOA. In addition, in order to estimate PM 2.5 concentration in small areas, [31][32][33] proposed a PM 2.5 estimation method based on MODIS original band data with 250 m resolution, a convolution neural network PM 2.5 estimation method based on Google images, and a convolution neural network and random forest coupling model based on the combination of planetscope commercial satellite and meteorological data. The above methods based on the direct estimation of spectral information use the feature extraction and data fitting capabilities of methods such as deep neural networks, random forest models, convolutional neural networks, and random forest coupled models to achieve better PM 2.5 estimation results.…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

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Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

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Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

Cited by 51 publications

References 39 publications

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

ResNet-LSTM for Real-Time PM_2.5 and PM₁₀ Estimation Using Sequential Smartphone Images

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

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Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

Cited by 51 publications

References 39 publications

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

ResNet-LSTM for Real-Time PM2.5 and PM₁₀ Estimation Using Sequential Smartphone Images

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

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ResNet-LSTM for Real-Time PM_2.5 and PM₁₀ Estimation Using Sequential Smartphone Images