PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network

Abstract In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM conce...

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Autores principales:	Sangwon Chae, Joonhyeok Shin, Sungjun Kwon, Sangmok Lee, Sungwon Kang, Donghyun Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3830b8fdb2bc4eb888ec0b6ae92c3406
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spelling	oai:doaj.org-article:3830b8fdb2bc4eb888ec0b6ae92c34062021-12-02T17:52:32ZPM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network10.1038/s41598-021-91253-92045-2322https://doaj.org/article/3830b8fdb2bc4eb888ec0b6ae92c34062021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91253-9https://doaj.org/toc/2045-2322Abstract In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires. This combination creates the interpolated CNN (ICNN) model that we use to predict PM10 and PM2.5 concentrations. The PM10 and PM2.5 evaluation results show an effective prediction performance with an R-squared higher than 0.97 and a root mean square error (RMSE) of approximately 16% of the standard deviation. Furthermore, both PM10 and PM2.5 prediction models forecast high concentrations with high reliability, with a probability of detection higher than 0.90 and a critical success index exceeding 0.85. The proposed ICNN prediction model achieves a high prediction performance using spatio-temporal information and presents a new direction in the prediction field.Sangwon ChaeJoonhyeok ShinSungjun KwonSangmok LeeSungwon KangDonghyun LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sangwon Chae Joonhyeok Shin Sungjun Kwon Sangmok Lee Sungwon Kang Donghyun Lee PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
description	Abstract In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires. This combination creates the interpolated CNN (ICNN) model that we use to predict PM10 and PM2.5 concentrations. The PM10 and PM2.5 evaluation results show an effective prediction performance with an R-squared higher than 0.97 and a root mean square error (RMSE) of approximately 16% of the standard deviation. Furthermore, both PM10 and PM2.5 prediction models forecast high concentrations with high reliability, with a probability of detection higher than 0.90 and a critical success index exceeding 0.85. The proposed ICNN prediction model achieves a high prediction performance using spatio-temporal information and presents a new direction in the prediction field.
format	article
author	Sangwon Chae Joonhyeok Shin Sungjun Kwon Sangmok Lee Sungwon Kang Donghyun Lee
author_facet	Sangwon Chae Joonhyeok Shin Sungjun Kwon Sangmok Lee Sungwon Kang Donghyun Lee
author_sort	Sangwon Chae
title	PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
title_short	PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
title_full	PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
title_fullStr	PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
title_full_unstemmed	PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
title_sort	pm10 and pm2.5 real-time prediction models using an interpolated convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3830b8fdb2bc4eb888ec0b6ae92c3406
work_keys_str_mv	AT sangwonchae pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork AT joonhyeokshin pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork AT sungjunkwon pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork AT sangmoklee pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork AT sungwonkang pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork AT donghyunlee pm10andpm25realtimepredictionmodelsusinganinterpolatedconvolutionalneuralnetwork
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PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network

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