Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz, Ayman; Ahmed, Ali Najah; Zaini, Nur’atiah; Sherif, Mohsen; Sefelnasr, Ahmed; El-Shafie, Ahmed

doi:10.1080/19942060.2021.1926328

Cited by 33 publications

(17 citation statements)

References 61 publications

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“…This is expected since extreme values occur less frequently, resulting in fewer training opportunities during the model training process. Consequently, the results of the XGBoost model tend to be conservative, and there may be some deficiencies in capturing extreme maximum and minimum values, as also shown in previous studies (Jumin et al, 2020;Ma et al, 2022;Yafouz et al, 2021).…”

Section: Uncertainty and Robustness And Analysis Of Xgboost Modelmentioning

confidence: 85%

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Luo,

Lu,

Chen

et al. 2024

Earth and Space Science

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Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8‐hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission‐related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 μg m−3.

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Section: Uncertainty and Robustness And Analysis Of Xgboost Modelmentioning

confidence: 85%

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Luo,

Lu,

Chen

et al. 2024

Earth and Space Science

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“…Finally, we would like to further discuss the importance of the choice of driving variables in ozone forecasting. For machine learning-based ozone forecasting at stations, the model performance may be improved with increasing inputs (Su et al, 2020;Yafouz et al, 2021). In contrast, for spatio-temporal ozone forecasting, with the increase of the number of driving factors, the number of modeling parameters to be optimially estimated will significantly increase, especially on a large spatial scale.…”

Section: B2 Impact Of No 2 Emissions On Ozone Forecastingmentioning

confidence: 99%

Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

Cheng

Fang

Navon

et al. 2022

J Adv Model Earth Syst

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Efficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we introduce a hybrid model (VAE‐GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE‐GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead‐time beyond the training period. The performance of VAE‐GAN is demonstrated in hourly and daily spatio‐temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE‐GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE‐GAN is a cost‐effective tool for large data‐driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

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“…To assess the performance of the proposed models in PM 2.5 prediction, different statistical matrices are employed as shown below [71][72][73]:…”

Section: Performance Evaluationmentioning

confidence: 99%

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

AlOmar

Khaleel

Al-Saadi

et al. 2022

Advances in Meteorology

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Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.

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Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Cited by 33 publications

References 61 publications

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

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