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Related Experiment Video

Updated: Nov 21, 2025

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting.

Yi-Chung Hu^1,2, Peng Jiang³, Jung-Fa Tsai⁴

¹College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

International Journal of Environmental Research and Public Health

|January 15, 2021

Summary

This study optimizes grey prediction models for improved accuracy in forecasting real-world data. By using a genetic algorithm and fractional-order accumulation, the enhanced model significantly outperforms traditional methods for carbon dioxide emission predictions.

Keywords:

carbon dioxide emissions forecasting fractional-order genetic algorithm grey theory

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Area of Science:

Environmental Science
Data Science
Mathematical Modeling

Background:

Grey prediction models are useful for data not conforming to statistical distributions.
The standard GM(1,1) grey prediction model has limitations in parameter determination and equal sample weighting.
Accurate forecasting is crucial for environmental and economic policy, particularly for emissions data.

Purpose of the Study:

To enhance the accuracy and applicability of grey prediction models.
To address limitations in parameter estimation and data weighting within the GM(1,1) model.
To develop a superior prediction model for carbon dioxide emissions using optimized grey prediction techniques.

Main Methods:

Developed an optimized grey prediction model incorporating a genetic algorithm for parameter optimization.
Implemented fractional-order accumulation to assign differential weights to sample data.
Validated the model using International Energy Agency carbon dioxide emission data.

Main Results:

The proposed optimized grey prediction model demonstrated significantly superior performance compared to existing models.
The genetic algorithm effectively determined crucial model parameters, overcoming background value limitations.
Fractional-order accumulation improved the model's ability to capture data regularities.

Conclusions:

The optimized grey prediction model offers a more robust and accurate forecasting tool.
This approach provides a significant advancement in grey system prediction methodologies.
The findings have implications for more precise environmental emission monitoring and policy-making.