Modelling the relationship between population density and air quality using fractional Hausdorff grey multivariate model

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kaihe Shi ◽  
Lifeng Wu
Keyword(s):  
Air Quality ◽  
Population Density ◽  
Composite Index ◽  
Original Data ◽  
Multivariate Model ◽  
Grey Model ◽  
Content Type ◽  
New Information ◽  
Proposed Model ◽  
Comparison Results

Purpose The proposed model can emphasize the priority of new information and can extract messages from the first pair of original data. The comparison results show that the proposed model can improve the traditional grey model. Design/methodology/approach The grey multivariate model with fractional Hausdorff derivative is firstly put forward to enhance the forecasting accuracy of traditional grey model. Findings The proposed model is used to predict the air quality composite index (AQCI) in ten cities respectively. Originality/value The effect of population density on AQCI in cities with poor air quality is not as significant as that of the cities with better air quality.

What makes users willing or hesitant to use Fintech?: the moderating effect of user type

2018 ◽  
Vol 118 (3) ◽  
pp. 541-569 ◽  
Author(s):  
Hyun-Sun Ryu
Keyword(s):  
Least Squares Method ◽  
Original Data ◽  
The Novel ◽  
Continuance Intention ◽  
Content Type ◽  
Factors Affecting ◽  
Proposed Model ◽  
Negative Effect ◽  
Partial Least Squares Method ◽  
Benefit And Risk

Purpose The purpose of this paper is to better understand why people are willing or hesitant to use Financial technology (Fintech) as well as to determine whether the effect of perceived benefits and risks of continuance intention differs depending on user types. Design/methodology/approach Original data were collected via a survey of 243 participants with Fintech usage experience. The partial least squares method was used to test the proposed model. Findings The results reveal that legal risk had the most negative effect on the Fintech continuance intention, while convenience had the strongest positive effect. Differences in specific benefit and risk impacts are found between early and late adopters. Originality/value This empirical study contributes to the novel understanding of the benefit and risk factors affecting the Fintech continuance intention.

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wen-ze Wu ◽  
Wanli Xie ◽  
Chong Liu ◽  
Tao Zhang
Keyword(s):  
Wind Turbine ◽  
Nonlinear Coefficient ◽  
Predictive Performance ◽  
Prediction Performance ◽  
New Method ◽  
Grey Model ◽  
Content Type ◽  
Proposed Model ◽  
Whale Optimization ◽  
Turbine Capacity

PurposeA new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.Design/methodology/approachFirst of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.FindingsThe empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.Originality/valueBy combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

scholarly journals A Novel Grey Power-markov Model for the Prediction of China's Electricity Consumption

2021 ◽  
Author(s):  
Liqin Sun ◽  
Youlong Yang ◽  
Tong Ning ◽  
Jiadi Zhu
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Original Data ◽  
Development Trend ◽  
Statistical Hypothesis ◽  
Grey Prediction ◽  
New Information ◽  
Proposed Model ◽  
Grey Models

Abstract The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

Integration effects of UAVs in non-segregated airspace

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tamer Savas ◽  
Oznur Usanmaz ◽  
Ozlem Sahin ◽  
Ertan Çınar ◽  
Murat Karaderili
Keyword(s):  
Real Time ◽  
Simulation Method ◽  
Flight Time ◽  
Flight Distance ◽  
Content Type ◽  
Time Simulation ◽  
Proposed Model ◽  
Comparison Results ◽  
Quantitative Results ◽  
Real Time Simulations

Purpose The study aims to design a new route model for unmanned aerial vehicles (UAVs) to integrate them into non-segregated airspace. Design/methodology/approach The proposed route model was assessed and validated through real-time simulations. Findings The comparison results of baseline and proposed route model show that a reduction of 38% and 41% in the total flight time and total flight distance were obtained in favour of the proposed model, respectively. Practical implications The proposed route model can be applied by airspace designers and UAV users to perform safe and efficient landing in non-segregated airspace. Originality/value In this study, a new proposed route model is constructed for UAVs. Quantitative results, using a real-time simulation method, are achieved in terms of flight distance and flight time.

scholarly journals Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Li ◽  
Han Xie
Keyword(s):  
Energy Consumption ◽  
Maximum Degree ◽  
Original Data ◽  
Grey Model ◽  
Variable Weights ◽  
Proposed Model ◽  
Term Energy ◽  
Long Term Forecasting ◽  
The Stability

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

scholarly journals Application of a New Information Priority Accumulated Grey Model with Simpson to Forecast Carbon Dioxide Emission

2020 ◽  
pp. 70-83 ◽  
Author(s):  
Xiwang Xiang ◽  
Yubin Cai ◽  
Shuchuan Xie
Keyword(s):  
Carbon Dioxide ◽  
Climate Warming ◽  
Carbon Dioxide Emissions ◽  
Human Life ◽  
Carbon Dioxide Emission ◽  
Grey Model ◽  
New Information ◽  
Proposed Model ◽  
Accurate Forecast ◽  
Work First

Climate warming is a hot topic of common concern all over the world and it has had a significant impact on climate, oceans and human life. The increase in the concentration of carbon dioxide in the atmosphere has become a significant factor in climate warming. In recent years, the concentration of carbon dioxide in the atmosphere has been mostly anthropogenic emissions. Accurate forecasting of carbon dioxide emissions will effectively propose solutions to the problem of global warming and then improve the environment in which we live. In our work, first of all, we use the new information priority accumulation method to optimize the weight of the new information in the prediction. Then we use the numerical integration method to optimize the background value of the grey model to achieve more accurate forecast. Application case results show that our proposed model is superior to other grey models in predicting carbon dioxide emission in India and Bangladesh.

An improved discrete grey multivariable model for forecasting the R&D output of China—from the perspective of R&D institutions

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jue Wang ◽  
Wuyong Qian
Keyword(s):  
Science And Technology ◽  
Development Trend ◽  
Interaction Effects ◽  
Multivariable Model ◽  
Content Type ◽  
Industrial Enterprises ◽  
Proposed Model ◽  
Comparison Results ◽  
Further Development

PurposeThe purpose of this study is to make a prediction of the R&D output of China from the perspective of R&D institutions and put forward a set of policy recommendations for further development of the science and technology in China.Design/methodology/approachIn this paper, an improved discrete grey multivariable model is proposed, which takes both the interaction effects and the accumulative effects into account. As the current research on China's R&D activities is generally based on the perspective of universities or industrial enterprises above designated size while few studies put their focus on R&D institutions, this paper applies the proposed model to carry out an empirical analysis based on the data of China's R&D institutions from 2009 to 2019. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of the R&D output in China's R&D institutions is conducted into a future horizon from 2020 to 2023 by using the model.FindingsThe results indicate that China's R&D institutions have a good development trend and broad prospects, which is closely related to China's long-term investment in science and technology. Additionally, the R&D inputs of China possess obvious interaction effects and accumulative effects.Originality/valueThe paper considers the interaction effects and the accumulative effects of R&D inputs simultaneously and proposes an improved discrete grey multivariable model, which fills the gap in previous studies.

Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiwang Xiang ◽  
Xin Ma ◽  
Minda Ma ◽  
Wenqing Wu ◽  
Lang Yu
Keyword(s):  
Real World ◽  
Pm10 Concentration ◽  
Euler Polynomial ◽  
Grey Model ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
New Model ◽  
Proposed Model ◽  
Grey Models

PurposePM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.Design/methodology/approachThe grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.FindingsThe introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.Practical implicationsWith high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.Originality/valueThis is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

scholarly journals A New Information Priority Accumulated Grey Model with Hyperbolic Sinusoidal Term and its Application

2021 ◽  
Vol 1 (2) ◽  
pp. 5-19
Author(s):  
Xue Tian ◽  
Wenqing Wu ◽  
Xin Ma ◽  
Peng Zhang
Keyword(s):  
Natural Gas ◽  
Fossil Fuels ◽  
Optimal Parameter ◽  
Estimation Method ◽  
Least Square ◽  
Grey Model ◽  
Grey Forecasting ◽  
New Information ◽  
Proposed Model ◽  
Whale Optimization

Compared to fossil fuels, natural gas is cleaner energy, which has developed rapidly in recent years. Studying the urban supply of natural gas has implications for the development of natural gas. In this paper, the new information priority accumulation method is integrated into the grey forecasting model with the hyperbolic sinusoidal driving term, and then the new grey model is used to predict the urban natural gas supply. The system's linear parameters are calculated by the least square estimation method, and the optimal parameter of the new information accumulated priority is determined by the Whale Optimization Algorithm. Finally, the supply of urban gas is forecasted using the proposed model, and comparative analyses with the four other forecasting models are presented.  

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