A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios

Xu, Shan; Zou, Bin; Shafi, Sedra; Sternberg, Troy

doi:10.1016/j.atmosenv.2018.06.014

Cited by 48 publications

(15 citation statements)

References 45 publications

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“…China is experiencing severe aerosol pollution, and numerous studies on aerosol pollution have utilized MODIS Collection 6.0 aerosol retrievals to map aerosol pollution and to analyze its spatiotemporal trends (Fang et al, 2016;Ma et al, 2014;He and Huang, 2018a, b;Zou et al, 2016Zou et al, , 2019Zhai et al, 2018). Few studies have applied 1 km MAIAC aerosol retrievals to map finer aerosol concentrations in regional China, e.g., the Yangtze River Delta (Xiao et al, 2017) and Shandong Province (Li et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

et al. 2019

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Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

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Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

et al. 2019

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“…In Reference [22], an improved Grey-Markov model based on wavelet transform was developed to achieve accurate prediction of China's energy supply and demand. Until now, the advantages of Grey-Markov over other prediction models, such as convenient parameters training, low computing time, and high forecasting accuracy, have been validated by massive experimental researches [23,24]. Based on this, the Grey-Markov model is conducive to obtain optimal solutions.…”

Section: Introductionmentioning

confidence: 97%

“…Finally, based on the obtained RIMFs, an IGMMW prediction model is developed to efficiently forecast the future degradation trend of engines. Because of the combination of moving window method, the problem of circular update for sequences being modeled, which occurs in [21][22][23][24], can be solved well. Besides, the adaptive parameter in moving window, i.e., the step size, is helpful to the implementation of multistep prediction to further improve the computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

et al. 2019

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As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

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“…(a) Optimization of GM(1,1) parameters: such as initial condition optimization [13,14], background value optimization [15,16], and accumulation order optimization [17][18][19] (b) Optimization of GM(1,1) structure: realizing the optimization of model structure from the single exponential form to intelligent variable structure [20][21][22] (c) Extension of GM(1,1) modeling object: to achieve the expansion of modeling objects from real data to grey uncertain data [23][24][25] (d) GM (1,1) combined forecasting model: the combination prediction technologies of GM (1,1) and other methods are studied, such as Grey neural network model [26][27][28], Grey Markov model [29,30], Grey support vector machine [31,32], and Grey deep learning [33,34] e above research results play an important role in improving the simulation and prediction performance and expanding the application scope of GM (1,1). However, GM(1,1) is a grey model with incomplete structural information (the absence of independent variables).…”

Section: Introductionmentioning

confidence: 99%

Modeling Method of the Grey GM(1,1) Model with Interval Grey Action Quantity and Its Application

Zeng

Shi

2020

Complexity

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GM(1,1) is a univariate grey prediction model with incomplete structural information, in which the real number form of the simulation or prediction data does not conform to the Nonuniqueness Principle of Grey theoretical solution. In light of the network model of GM(1,1), the connotation of grey action quantity is systematically analyzed and the interval grey number form of grey action quantity is restored under uncertain influencing factors. A novel GM(1,1) model is then constructed. The new model has the basic characteristics of the grey model under incomplete information. Moreover, it can be fully compatible with the traditional GM(1,1) model. The developed model is employed to the natural gas consumption prediction in China, showing that its predicting rationality is much better than that of the traditional GM(1,1) model. It is worth mentioning that, for the first time, the grey property of GM(1,1) has been restored in structure, which is of significance for both academia and industry.

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A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios

Cited by 48 publications

References 45 publications

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

Modeling Method of the Grey GM(1,1) Model with Interval Grey Action Quantity and Its Application

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