A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Xiong, Pingping; Shi, Jianzong; Pei, Ling-Ling; Ding, Song

doi:10.3390/su11143832

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…The data included irrigation channel water quality, sediment TM concentrations, paddy soil TM concentrations, and other data. The model calibration and verification used mean absolute percentage error (MAPE) as a measure of quality for this model [34]:…”

Section: Model Calibration and Verificationmentioning

confidence: 99%

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

et al. 2020

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In this study, the water quality of the irrigation system and concentration of trace metals in the sediments were combined to establish a farmland trace metal contamination and management model (FTM_CMM). The purpose of this model was to clarify the main sources of the trace metals that have caused the contamination of paddy soil in central Taiwan. The results of the model simulation showed that the trace metals in the paddy soil mainly came from the irrigation water and especially from the sediments in the irrigation channels. The contribution of the sediments in the irrigation channel to the individual trace metals in the paddy soil ranged from 56% to 72% as the contributions for Cr, Cu, Ni, and Zn were 72%, 68%, 56%, and 62%, respectively. The trace metal species according to their concentration in the contaminated soil ranked in the order of Zn > Cr ≈ Cu > Ni, which is about the same as in the channel sediment. During the simulation process, Cr, Cu, Ni, and Zn exceeded the control standards for farmlands in the 18th, 12th, 13th, and 17th years, respectively. This highlights that, in addition to the management of irrigation water quality, the management of trace metal contaminated sediment in adjacent canal irrigation systems is also an important part of the prevention of trace metal contamination in farmland.

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Section: Model Calibration and Verificationmentioning

confidence: 99%

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

et al. 2020

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“…Xu et al (2018) used a combination of a grey Markov model and a land use regression model to predict changes in PM 10 concentrations [16]. used an optimized grey extended prediction model to predict smog pollution in Shanghai and Beijing, China [17,18]. Shi and Wu (2021) introduced the Hausdorff derivative to the cumulative operator of the grey prediction model and the proposed new fractional-order grey prediction model was applied to predict the air quality indicators of cities [19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Air Quality Evolution Trends in the Chinese Air Pollution Transmission Channel Cities under Socioeconomic Development Scenarios

et al. 2023

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In this paper, based on the traditional grey multivariate convolutional model, the concept of a buffer operator is introduced to construct a single-indicator buffered grey multivariate convolutional model applicable to air quality prediction research. The construction steps of the model are described in detail in this paper, and the stability of the model is analyzed based on perturbation theory. Furthermore, the model was applied to predict the air quality composite index of the “2 + 26” Chinese air pollution transmission corridor cities based on different socioeconomic development scenarios in a multidimensional manner. The results show that the single-indicator buffered grey multivariate convolutional model constructed in this paper has better stability in predicting with a small amount of sample data. From 2020 to 2025, the air quality of the target cities selected in this paper follows an improving trend. The population density, secondary industry, and urbanization will not have a significant negative impact on the improvement of air quality if they are kept stable. In the case of steady development of secondary industry, air quality maintained a stable improvement in 96.4% of the “2 + 26” cities. The growth rate of population density will have an inverted U-shaped relationship with the decline in the city air quality composite index. In addition, with the steady development of urbanization, air quality would keep improving steadily in 71.4% of the “2 + 26” cities.

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“…Reverse accumulation used more information about the new data than the old data, which can effectively improve the performance of the model (Liao & Luo 2011). Combining the advantages of these two aspects, this study used the fractional order reverse accumulative grey model to predict regional water demand, which had good research prospects and has attracted the attention of many scholars (Xiong et al 2019). And fractional order reverse accumulative grey model had less predictive disturbance and could utilize new information of the sequence compared with the fractional-order forward accumulation model and the first-order backward accumulation model.…”

Section: Introductionmentioning

confidence: 99%

Forecast of regional water demand based on NSGAII-FORAGM

Liu

2021

Water Supply

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Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method were adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (The tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, 1.8% respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (The relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

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A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Cited by 15 publications

References 30 publications

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

Analysis of Air Quality Evolution Trends in the Chinese Air Pollution Transmission Channel Cities under Socioeconomic Development Scenarios

Forecast of regional water demand based on NSGAII-FORAGM

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