A multivariate discrete grey model for estimating the waste from mobile phones, televisions, and personal computers in India

Kiran, Medikonda Asha; Venkatesan, Shanmugam Prasanna; Mishra, Atul; Mehendale, Abhang; Sherin, H.R. Nadheera

doi:10.1016/j.jclepro.2021.126185

Cited by 27 publications

(8 citation statements)

References 58 publications

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“…The GM model is a vital component of the grey system theory put forward by the famous Chinese scholar Deng [1]. Due to its excellent predictive performance, the GM model and its variants have been widely used in various areas of society [2][3][4][5][6][7]. The GM (1,1) model is the benchmark model of all grey variant models.…”

Section: Introductionmentioning

confidence: 99%

The development trend of China’s aging population: a forecast perspective

Liu

Zhu

Zou

2022

Complex Intell. Syst.

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To accurately predict the aging population in China, a novel grey prediction model (CFODGMW(1,1, $$\alpha $$ α ) model) is established in this study. The CFODGMW(1,1, $$\alpha $$ α ) model has all the advantages of the weighted least square method, combined fractional-order accumulation generation operation and grey prediction model with time power term, which makes it have excellent prediction performance. Compared with the traditional grey prediction model based on the least square method and the first-order accumulation operation, the CFODGMW(1,1, $$\alpha $$ α ) model has stronger adaptability. The proposed model and its competing models are used to analyze the aging population in five regions of China. The results show that the prediction performance of the CFODGMW(1,1, $$\alpha $$ α ) model is better than other models. Based on this, the CFODGMW(1,1, $$\alpha $$ α ) model is used to predict the aging population in China in the next 4 years, and some suggestions are given based on the development trend of the aging population.

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Section: Introductionmentioning

confidence: 99%

The development trend of China’s aging population: a forecast perspective

Liu

Zhu

Zou

2022

Complex Intell. Syst.

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“…Because there is less annual data, the grey model can be used to solve this problem. Grey prediction models are widely used, because of their effectiveness in short time series prediction [14][15][16]. Among them, the grey multivariate model has greatly improved the prediction accuracy due to the introduction of influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Air Pollution and the Public Perception Level and Self-Protection Demand in Three Cities of China: Fractional Grey Modelling Analysis

Zhao

2021

Journal of Mathematics

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To improve air pollution and human health, a novel grey prediction model with fractional-order accumulation and new information priority accumulation is proposed to analyze and predict the public perception level, self-protection demands, and environmental protection behavior of air pollution. The study in three cities of China shows that the public’ perception level will rise rapidly, and the speed will continue to accelerate with the further deterioration of air quality. Among them, Beijing’s public perception is the most sensitive and strong. The protection needs of the Tianjin public are the most sensitive. The Shijiazhuang public shows a strong desire for environmental protection. This study will help in understanding the relationship between the public and air quality.

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“…Mao et al (2020) established a time-space ARMA model for e-waste forecasting by considering both the temporal and spatial correlations of e-waste recycling outlets. To solve the problem of 'small sample' forecasting modelling, Kiran et al (2021) estimated and predicted the discarded quantity of five types of products, including TVs, washing machines and air conditioners, from 2007 to 2017 using a grey prediction model and market supply model. Zhao et al (2016) estimated and predicted the waste of TVs, washing machines and air conditioners in China from 2015 to 2041 using a consumption-use model and an integrated grey model (GM) first-order one-variable GM (1,1).…”

Section: Introductionmentioning

confidence: 99%

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

et al. 2021

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Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

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A multivariate discrete grey model for estimating the waste from mobile phones, televisions, and personal computers in India

Cited by 27 publications

References 58 publications

The development trend of China’s aging population: a forecast perspective

The development trend of China’s aging population: a forecast perspective

Air Pollution and the Public Perception Level and Self-Protection Demand in Three Cities of China: Fractional Grey Modelling Analysis

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

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