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Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numer...

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Autores principales: Ko, Kabseok, Cho, Seokheon, Rao, Ramesh R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610675/
https://www.ncbi.nlm.nih.gov/pubmed/36298214
http://dx.doi.org/10.3390/s22207864
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author Ko, Kabseok
Cho, Seokheon
Rao, Ramesh R.
author_facet Ko, Kabseok
Cho, Seokheon
Rao, Ramesh R.
author_sort Ko, Kabseok
collection PubMed
description Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations.
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spelling pubmed-96106752022-10-28 Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information Ko, Kabseok Cho, Seokheon Rao, Ramesh R. Sensors (Basel) Article Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations. MDPI 2022-10-16 /pmc/articles/PMC9610675/ /pubmed/36298214 http://dx.doi.org/10.3390/s22207864 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ko, Kabseok
Cho, Seokheon
Rao, Ramesh R.
Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title_full Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title_fullStr Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title_full_unstemmed Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title_short Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
title_sort machine-learning-based near-surface ozone forecasting model with planetary boundary layer information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610675/
https://www.ncbi.nlm.nih.gov/pubmed/36298214
http://dx.doi.org/10.3390/s22207864
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