A Haze Feature Extraction and Pollution Level Identification Pre-warning Algorithm

Zhang, Yongmei; Ma, Jun; Hu, Lei; Yu, Keming; Song, Lihua; Chen, Huini

doi:10.32604/cmc.2020.010556

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Nevertheless, the above preceding approaches also have many defects, such as the SVMs running speed is slow, the choice of kernel function and other associated factors have a major influence on the performance. Several researchers employ heuristic techniques to improve the performance, but they still have sluggish convergence speed and fall into the local optimization dilemma (Dai et al, 2021;Zhang et al, 2020). While the typical machine learning model does not provide satisfactory results, the deep learning model has been extensively adopted.…”

mentioning

confidence: 99%

Haze Prediction Method Based On Stacking Learning

Liu

Zhao

2023

Preprint

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In recent years, with the rapid economic development of our country, environmental problems have become increasingly prominent, especially air pollution has more and more affected People's daily life. Air pollution is mobile and can cause long-term effects over large areas, which are detrimental to the natural environment and human body. Haze is a form of air pollution, which comprises PM2.5 components that adversely impair human health. Multiple approaches for predicting PM2.5 in the past have had limited accuracy, meanwhile required vast quantities of data and computational resources. In order to tackle the difficulties of poor fitting effect, large data demand, and slow convergence speed of prior prediction techniques, a PM2.5 prediction model based on the stacking integration method is proposed. This model employs eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) as the base model, while ridge regression is used as the meta-learner to stack. PM2.5 concentration is influenced by a variety of pollutant factors and meteorological factors, and the correlation between PM2.5 concentration and other factors was analyzed using Spearman's correlation coefficient method. Several significant factors that determine the haze concentration are selected out, and the stacking model is built on this data for training and prediction. The experimental results indicate that the fusion model constructed in this thesis can provide accurate PM2.5 concentration estimates with fewer data features. This approach performs better in terms of accuracy and time consumption.

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mentioning

confidence: 99%

Haze Prediction Method Based On Stacking Learning

Liu

Zhao

2023

Preprint

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Haze prediction method based on stacking learning

Liu,

Zhao

2023

Stoch Environ Res Risk Assess

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In recent years, with the rapid economic development of our country, environmental problems have become increasingly prominent, especially air pollution has more and more affected People’s daily life. Air pollution is mobile and can cause long-term effects over large areas, which are detrimental to the natural environment and human body. Haze is a form of air pollution, which comprises PM2.5 components that adversely impair human health. Multiple approaches for predicting PM2.5 in the past have had limited accuracy, meanwhile required vast quantities of data and computational resources. In order to tackle the difficulties of poor fitting effect, large data demand, and slow convergence speed of prior prediction techniques, a PM2.5 prediction model based on the stacking integration method is proposed. This model employs eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) as the base model, while ridge regression is used as the meta-learner to stack. PM2.5 concentration is influenced by a variety of pollutant factors and meteorological factors, and the correlation between PM2.5 concentration and other factors was analyzed using Spearman’s correlation coefficient method. Several significant factors that determine the haze concentration are selected out, and the stacking model is built on this data for training and prediction. The experimental results indicate that the fusion model constructed in this thesis can provide accurate PM2.5 concentration estimates with fewer data features. The RMSE of the proposed model is 19.2 and the R2 reached 0.94, an improvement of 3–25% over the single model. This hybrid model performs better in terms of accuracy.

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Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement

Zhang,

Gao

et al. 2024

Applied Soft Computing

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A Haze Feature Extraction and Pollution Level Identification Pre-warning Algorithm

Cited by 3 publications

References 17 publications

Haze Prediction Method Based On Stacking Learning

Haze Prediction Method Based On Stacking Learning

Haze prediction method based on stacking learning

Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement

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