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A data-driven approach to the forecasting of ground-level ozone concentration.
TLDR
A machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration for several geographical locations in southern Switzerland suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables, which are described in the ozone photochemistry literature.Abstract:
The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighbourhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions; our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps in increasing the accuracy of the forecasts for specific ranges of ozone's daily peak values.read more
Citations
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Multi-source and multivariate ozone prediction based on fuzzy cognitive maps and evidential reasoning theory
TL;DR: In this paper , a multi-source and multivariate ozone prediction model based on fuzzy cognitive maps (FCMs) and evidential reasoning theory from the perspective of spatio-temporal fusion, termed as ERC-FCM, is proposed.
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Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model
TL;DR: In this article , a prediction method based on the KNN-Prophet-LSTM hybrid model is established by using the daily pollutant concentration data of Wuhan from January 1, 2014, to May 3, 2021, and considering the characteristics of time and space.
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A comparison of machine learning methods for ozone pollution prediction
Fouzi Harrou,Ying Sun +1 more
TL;DR: In this paper , the authors evaluated the predictive performance of nineteen machine learning models for ozone pollution prediction and investigate using time-lagged measurements to improve prediction accuracy, showing that dynamic models using timelagged data outperformed static and reduced machine learning.
References
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Book
The Mechanisms of Reactions Influencing Atmospheric Ozone
TL;DR: In this paper, the authors present a review of the chemical mechanisms for air quality modeling and their application in the field of air quality modelling and air quality analysis. But their focus is on ozone in the Troposphere.
Journal ArticleDOI
Reliability diagrams for non-parametric density forecasts of continuous variables: Accounting for serial correlation
TL;DR: In this paper, the authors proposed to use a surrogate consistency resampling method for the reliability assessment of non-parametric density forecasts of short-term wind-power generation.
Journal ArticleDOI
Decomposition and graphical portrayal of the quantile score
TL;DR: It is argued that the QS is ready to become as popular as the Brier score in forecast verification and its decomposition is illustrated on precipitation forecasts derived from the mesoscale weather prediction ensemble COSMO-DE-EPS of the German Meteorological Service.
Journal ArticleDOI
Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques
Bing Gong,Joaquín Ordieres-Meré +1 more
TL;DR: The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances and confirms that regional data facilitate better forecasting.
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Calculated Influence of Temperature-Related Factors on Ozone Formation Rates in the Lower Troposphere
Chris J. Walcek,Hong-H. Yuan +1 more
TL;DR: In this article, the sensitivities ozone (O3) formation rates were quantified for changes in four meteorologically controlled parameters: temperature, sunlight intensity, water vapor mixing ratio, and isoprene concentration.
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