A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

doi:10.1016/j.atmosenv.2016.03.056

Cited by 232 publications

(72 citation statements)

References 33 publications

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“…Machine learning and statistical models have been widely used in outdoor environments to predict the concentrations of atmospheric pollutants [20][21][22][23] and in indoor environments to predict thermal comfort [24][25][26] and building energy efficiency. [27][28][29] Models that have been commonly used in these studies include various regression models, partial least squares (PLS), decision trees (classification and regression trees), Bayesian hierarchical modeling, generalized boosting models, support vector machine, random forests, generalized linear models, and artificial neural networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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Section: Introductionmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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“…Qin et al built a hybrid model combining ensemble empirical mode decomposition (EEMD), cuckoo search (CS) and a back-propagation artificial neural network to implement PM forecasting, and the simulation revealed that the hybrid outperformed the benchmark models mentioned in the paper [11]. Niu et al proposed a novel hybrid decomposition-and-ensemble model based on complementary ensemble empirical mode decomposition (CEEMD), grey wolf optimizer and support vector regression (SVR) to perform PM 2.5 forecasting, and the empirical study illustrated that the proposed hybrid forecasting model was significantly superior to the benchmark models used in the paper [12]. Zhou et al presented a general regression neural network (GRNN) model combining EEMD.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Wang

Niu

Wang

2017

IJERPH

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The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

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“…This algorithm is inspired by the social hierarchy and hunting strategies of grey wolves in the wild. It can be regarded as a robust swarm-based optimizer [40][41][42][43][44][45]. The following discusses its working mechanism.…”

Section: Gwo Algorithmmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

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A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

Cited by 232 publications

References 33 publications

Machine learning and statistical models for predicting indoor air quality

Machine learning and statistical models for predicting indoor air quality

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

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