The forecast of C0&lt;inf&gt;2&lt;/inf&gt; emissions in China based on RBF neural networks

Li, Shourong; Zhou, Rongxi; Ma, Xin

doi:10.1109/indusis.2010.5565845

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Several studies have proposed applying machine learning and deep learning models to estimate anthropogenic CO 2 emissions, such as artificial neural networks (ANNs), generalized regression neural networks (GRNNs), random forests (RFs), radial basis functions (RBFs), and long short-term memory (LSTM) networks. The results have shown that these models demonstrated some prediction performances and potential for estimating anthropogenic CO 2 emissions [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 95%

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

Ji,

Song,

Lei

et al. 2024

Atmosphere

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The monitoring of anthropogenic CO2 emissions, which increase the atmospheric CO2 concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO2 emissions. Training samples, which are used to model predictions of anthropogenic CO2 emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO2 emissions. We propose an approach for predicting anthropogenic CO2 emissions using the training datasets derived from the clustering of the atmospheric CO2 concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO2 emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO2-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO2 emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO2 emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO2 emissions could help us quickly obtain up-to-date information on anthropogenic CO2 emissions as one of the emission monitoring tools.

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Section: Introductionmentioning

confidence: 95%

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

Ji,

Song,

Lei

et al. 2024

Atmosphere

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“…Bo et al [5] proposed a method for predicting gas content based on the RBF neural network optimized by a genetic algorithm. Shourong et al [6] combined the RBF neural network with time series on CO 2 emissions to make a forecast of its emissions in China. Chuanbao and Fuwu [7] described an approach for replacing the engine out NO x sensor with a radial basis function neural network (RBFNN) based NO x perception.…”

Section: Introductionmentioning

confidence: 99%

Industrial pollution prediction by Radial Basis Function Networks

Djebbri¹,

Rouaïnia²

2018

Environ. Protect. Eng.

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Atmospheric pollution has been receiving a significant interest for several decades since industries cause more and more pollution. Thanks to the development of many prediction techniques, scientists and industries are focusing more on pollution prediction. The aim of this work is to predict the two pollutant concentrations (NOx and CO) in industrial sites by a modified radial basis function (RBF) based neural network. The modification considered the spread parameter h of the activation function in the RBF network. In order to get the best network, the variations of this parameter for three cases were considered. In the first case, only pollutants concentrations variables were used, while in the second one, only the meteorological variables were utilized. In the third case, pollutants' concentrations were connected with meteorological variables. Based on calculation errors, the best model that ensures the best monitoring of pollutants concentration could be identified.

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Modelling and forecasting of carbon-dioxide emissions in South Africa by using ARIMA model

Kour

2022

Int. J. Environ. Sci. Technol.

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The forecast of C0<inf>2</inf> emissions in China based on RBF neural networks

Cited by 4 publications

References 10 publications

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

Industrial pollution prediction by Radial Basis Function Networks

Modelling and forecasting of carbon-dioxide emissions in South Africa by using ARIMA model

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