Enhancing Quarterly Carbon Emission Forecasting in China：A small sample decomposition model based Caputo fractional derivative grey Riccati model and LSSVR DOI Creative Commons

Yue Sun,

Yonghong Zhang

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 15, 2023

Abstract Accurately predicting carbon emissions is a crucial scientific foundation for the monitoring and evaluation of country's progress in achieving its intended reduction goals. Given constraints small sample size, nonlinearity, complexity inherent quarterly data on at industrial level, this paper introduces Caputo fractional derivative into grey Riccati model, establishing model with memory characteristics. The numerical solution acquired through Adams-Bashforth-Moulton predictor-corrector algorithm, model's parameters optimized using Wolf optimization algorithm. Subsequently, integrated EEMD decomposition algorithm least square support vector regression to construct decomposition-integration emission decomposition. Finally, proposed decomposition-integrationmodel validated from six industries China as an illustrative example. results convincingly demonstrate that prediction effectively analyzes developmental trajectory China. Moreover, it exhibits superior stability accuracy both fitting forecasting when compared other single models.

Language: Английский

Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine DOI
Awomuti Adeboye, Zhen Yu, Adesina Odunayo Blessing

et al.

SAR and QSAR in environmental research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: March 24, 2025

Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces novel approach predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, preservatives, on PPARγ. The predictive model, based light-gradient boosting machine (LightGBM) algorithm, was trained dataset 1804 molecules showed r2 values 0.82 0.59, Mean Absolute Error (MAE) 0.38 0.58, Root Square (RMSE) 0.54 0.76 for training test sets, respectively. provides insights into interactions between emerging PPARγ, highlighting potential hazards risks these chemicals may pose public environment. ability inhibition by hazardous demonstrates value this guiding enhanced toxicology research risk assessment.

Language: Английский

Citations

0
Research on Regional Carbon Emission Prediction Method Based on GRA-PCA-Transformer DOI
Zhen Liu, Zhewang Ma, Lei Yang

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 45 - 55

Published: Jan. 1, 2025

Language: Английский

Citations

0
A hybrid model for point and interval forecasting of agricultural price based on the decomposition-ensemble and KDE DOI
Dabin Zhang, Xuejing Zhang, Huanling Hu

et al.

Soft Computing, Journal Year: 2024, Volume and Issue: 28(17-18), P. 10153 - 10176

Published: July 18, 2024

Language: Английский

Citations

3
Dual ensemble system for polyp segmentation with submodels adaptive selection ensemble DOI Creative Commons

Cun Xu,

Kefeng Fan, Wei Mo

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 14, 2024

Abstract Colonoscopy is one of the main methods to detect colon polyps, and its detection widely used prevent diagnose cancer. With rapid development computer vision, deep learning-based semantic segmentation for polyps have been researched. However, accuracy stability some in polyp tasks show potential further improvement. In addition, issue selecting appropriate sub-models ensemble learning task still needs be explored. order solve above problems, we first implement utilization multi-complementary high-level features through Multi-Head Control Ensemble. Then, sub-model selection problem training, propose SDBH-PSO Ensemble optimization weights different datasets. The experiments were conducted on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-LaribPolypDB PolypGen. results that DET-Former, constructed based Ensemble, consistently provides improved across Among them, demonstrated superior feature fusion capability experiments, excellent capability. capabilities will continue significant reference value practical utility as networks evolve.

Language: Английский

Citations

2
A combined framework for carbon emissions prediction integrating online search attention DOI
Dabin Zhang, Zehui Yu, Liwen Ling

et al.

Journal of Intelligent & Fuzzy Systems, Journal Year: 2024, Volume and Issue: 46(4), P. 11153 - 11168

Published: March 15, 2024

As CO2 emissions continue to rise, the problem of global warming is becoming increasingly serious. It important provide a robust management decision-making basis for reductions carbon worldwide by predicting accurately. However, affected various factors, prediction challenging due its nonlinear and nonstationary characteristics. Thus, we propose combination forecast model, named CEEMDAN-GWO-SVR, which incorporates multiple features predict trends in China’s emissions. First, impact online search attention public health emergencies are considered prediction. Since different variables on lagged, grey relational degree used identify appropriate lag series. Second, irrelevant eliminated through RFECV. To address issue feature redundancy attention, dimensionality reduction method based keyword classification. Finally, evaluate proposed framework, four evaluation indicators tested machine learning models. The best-performed model (SVR) optimized CEEMDAN GWO enhance accuracy. empirical results indicate that framework maintains good performance both multi-scenario multi-step

Language: Английский

Citations

0
Data-Driven Approaches for Achieving Carbon Neutrality: Predictive Models for Reducing CO2 Emissions and Enhancing Industrial Sustainability DOI Creative Commons
Farzana Islam

Published: Jan. 1, 2024

In response to the escalating challenges posed by climate change and industrial inefficiency, this thesis presents a comprehensive investigation aimed at advancing predictive modeling of global CO2 emissions enhancing operational efficiency in steel manufacturing through Electric Arc Furnace (EAF) temperature optimization. Leveraging rich dataset sourced from World Development Indicators database alongside meticulously curated specific EAF operations, our study applies an innovative blend econometric machine learning techniques, including Pooled Ordinary Least Squares (Pooled OLS), Random Effects (RE), Fixed (FE), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The objective is twofold: refine emission forecasts establish reliable model for predicting flat bath production, critical determinant energy product quality. Our analysis elucidates complex dynamics governing emissions, identifying key factors such as renewable consumption, GDP per unit use, total greenhouse gas significant determinants. These insights not only contribute academic discourse on environmental sustainability but also provide solid foundation policymakers devise more effective strategies reduction. Concurrently, realm manufacturing, breaks new ground harnessing data predict unprecedented accuracy. This advancement holds implications conservation optimization, addressing urgent need practices. bridges gap between theoretical research practical application sets benchmark utilization data-driven approaches science engineering. By offering detailed comparison techniques their prowess, it guides future directions underscores potential sophisticated analytical methods tackling some most pressing challenges. Ultimately, role achieving sustainable future, providing valuable that can inform both policy process

Language: Английский

Citations

0
A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting DOI
Liling Zeng, Huanling Hu, Qingkui Song

et al.

Energy, Journal Year: 2024, Volume and Issue: 313, P. 133699 - 133699

Published: Nov. 2, 2024

Language: Английский

Citations

0
Research on quarterly carbon emission prediction in China based Caputo fractional derivative grey Riccati model and Least squares support vector regression DOI Creative Commons

Yue Sun,

Yonghong Zhang

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 19, 2023

Abstract Accurately predicting carbon emissions is a crucial scientific foundation for the monitoring and evaluation of country's progress in achieving its intended reduction goals. Given constraints small sample size, nonlinearity, complexity inherent quarterly data on at industrial level, this paper introduces Caputo fractional derivative into grey Riccati model, establishing model with memory characteristics. The numerical solution acquired through Adams-Bashforth-Moulton predictor-corrector algorithm, model's parameters optimized using Wolf optimization algorithm. Subsequently, integrated EEMD decomposition algorithm least square support vector regression to construct decomposition-integration emission decomposition. Finally, proposed validated from six industries China as an illustrative example. results convincingly demonstrate that prediction effectively analyzes developmental trajectory China. Moreover, it exhibits superior stability accuracy both fitting forecasting when compared other single models.

Language: Английский

Citations

0
Enhancing Quarterly Carbon Emission Forecasting in China：A small sample decomposition model based Caputo fractional derivative grey Riccati model and LSSVR DOI Creative Commons

Yue Sun,

Yonghong Zhang

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 15, 2023

Abstract Accurately predicting carbon emissions is a crucial scientific foundation for the monitoring and evaluation of country's progress in achieving its intended reduction goals. Given constraints small sample size, nonlinearity, complexity inherent quarterly data on at industrial level, this paper introduces Caputo fractional derivative into grey Riccati model, establishing model with memory characteristics. The numerical solution acquired through Adams-Bashforth-Moulton predictor-corrector algorithm, model's parameters optimized using Wolf optimization algorithm. Subsequently, integrated EEMD decomposition algorithm least square support vector regression to construct decomposition-integration emission decomposition. Finally, proposed decomposition-integrationmodel validated from six industries China as an illustrative example. results convincingly demonstrate that prediction effectively analyzes developmental trajectory China. Moreover, it exhibits superior stability accuracy both fitting forecasting when compared other single models.

Language: Английский

Citations

0