Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Kwon, Soon-Bark

doi:10.1016/j.jhazmat.2017.07.050

Cited by 150 publications

(41 citation statements)

References 28 publications

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“…In the linear regression analysis, the range of R 2 at six subway stations were 0.18-0.63. Nevertheless, the neural network model with present time variables has high R 2 of 0.54-0.81 (Park et al, 2018). Fig.…”

Section: Comparison With Other Modelsmentioning

confidence: 94%

“…Air pollution forecasting also is crucial for public health interventions and air pollution control policymaking. However, air quality forecasting is quite complex (Li et al, 2017a;Park et al, 2018). Apart from the rapid economic growth, air pollution is affected by unfavorable meteorological conditions (Al-Saadi et al, 2005;Gong and Ordieres-Meré, 2016;Li et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Guo

et al. 2020

Aerosol Air Qual. Res.

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Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi'an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi'an and Lanzhou, although the WANN model (R = 0.8846 for Xi'an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi'an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies.

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Section: Comparison With Other Modelsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Guo

et al. 2020

Aerosol Air Qual. Res.

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“…A summary of IAQ prediction studies using ANN is provided in concentration in a subway station. 62 In addition to the indoor PM concentrations at the previous sampling time, some studies also used indoor temperature and NO x concentration at the current time as inputs to predict indoor PM concentrations at the current time. 63 In these studies, data from the previous sampling time were simply used as normal input data rather than time series data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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“…Although most studies have focused on outdoor PM concentrations, many residents in the metropolis use public transportation, including the subway where indoor air quality affects the health of riders. Park et al [13] focused on indoor air quality of subway systems in the metropolis. However, it is difficult to obtain indoor PM data because of the deployment of the measurement systems.…”

Section: Integration Of Societal and Urban Information Into Predictionmentioning

confidence: 99%

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

Jang

Shin

Lee

et al. 2020

Sensors

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Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

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Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

Cited by 150 publications

References 28 publications

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Machine learning and statistical models for predicting indoor air quality

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

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