Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Yeo, Inchoon; Choi, Yunsoo; Lops, Yannic; Sayeed, Alqamah

doi:10.1007/s00521-021-06082-8

Cited by 37 publications

(20 citation statements)

References 40 publications

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“…Because CNN processes all inputs and outputs independently, they may not sufficiently generate accurate predictions when previous information, such as time series, is required. Therefore, RNN models combined with CNN models allowed us to use previous information as an input, resulting in more accurate predictions [7,25,36]. RNN, however, cannot reset internal state information that affects subsequent observations.…”

Section: Deep Learning Architecturementioning

confidence: 99%

“…Although the time-dependent nature of hourly concentrations, which show daily peak concentrations, may be a more accurate indicator of exposure than daily averages, few studies have examined the health effects stemming from exposure to hourly peak concentrations of air pollution. Existing studies, such as this one, indicate the importance of determining peak hourly concentrations of air pollution [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…To address concerns about air quality and the risks posed by high emissions, air pollution researchers have begun to apply various machine learning and deep learning algorithms to various fields of atmospheric sciences to more accurately predict PM 2.5 concentrations [7,[15][16][17]; and several studies have improved numerical models [18]. Many approaches have applied deep learning algorithms and implemented convolutional neural network (CNN) models capable of learning joint features and classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, as the GRU has fewer parameters than LSTM, its learning rate is faster than that of LSTM [24,25]. In fields such as air pollution concentration prediction, which uses time sequences, it is suitable to apply the classification or prediction of time series data using GRU networks among RNN architectures [7].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, to improve the accuracy of PM 2.5 prediction, we used a Gaussian filter that removes noise from the original image in the image-processing research field [26]; we propose a method of improving the prediction accuracy of daily maximum concentrations of PM 2.5 , which is currently a limitation of the already developed deep learning prediction system [7,18]. We propose a method of capturing maximum daily concentrations of PM 2.5 using a Gaussian filter, which will improve the prediction accuracy of the captured maximum daily concentrations of PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

Yeo

Choi

2021

Sustainability

Self Cite

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This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM2.5 concentrations. The purpose is to accurately predict high-peak PM2.5 concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak PM2.5 concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of PM2.5 in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak PM2.5 concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration PM2.5 prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high PM2.5 concentrations in real time.

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Section: Deep Learning Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

Yeo

Choi

2021

Sustainability

Self Cite

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Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Cheng

Zhang

Wenzel

et al. 2022

Neural Comput & Applic

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As the global economy is booming, and the industrialization and urbanization are being expedited, particulate matter 2.5 (PM2.5) turns out to be a major air pollutant jeopardizing public health. Numerous researchers are committed to employing various methods to address the problem of the nonlinear correlation between PM2.5 concentration and several factors to achieve more effective forecasting. However, a considerable space remains for the improvement of forecasting accuracy, and the problem of missing air pollution data on certain target areas also needs to be solved. Our research work is divided into two parts. First, this study presents a novel stacked ResNet-LSTM model to enhance prediction accuracy for PM2.5 concentration level forecast. As revealed from the experimental results, the proposed model outperforms other models such as boosting algorithms or general recurrent neural networks, and the advantage of feature extraction through residual network (ResNet) combined with a model stacking strategy is shown. Second, to solve the problem of insufficient air quality and meteorological data on some research areas, this study proposes the use of a correlation alignment (CORAL) method to carry out a prediction on the target area by aligning the second-order statistics between source area and target area. As indicated from the results, this model exhibits a considerable accuracy even in the absence of historical PM2.5 data in the target forecast area.

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Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network

Lops

Ghahremanloo

Pouyaei

et al. 2023

Neural Comput & Applic

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Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Cited by 37 publications

References 40 publications

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network

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