Machine learning-based time series models for effective CO2 emission prediction in India

Kumari, Surbhi; Singh, Sunil Kumar

doi:10.1007/s11356-022-21723-8

Cited by 58 publications

(37 citation statements)

References 41 publications

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“…PM exposure has become a global challenge, accounting for 8.8 million deaths annually across the world (Li et al 2015). Rigorous anthropogenic activities resulting in poor air quality have drawn worldwide attention in recent decades, especially in Asian nations (Li et al 2016;Naqvi et al 2020;Bilal et al 2021;Kumari and Singh 2022). Atmospheric PM 2.5 continues to pique attention worldwide because of its substantial impacts on human health, the environment, and welfare (Valavanidis et al, 2008;Hassanen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

Islam

Awadh

Mallick

et al. 2023

Air Qual Atmos Health

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Fine particulate matter (PM 2.5 ) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM 2.5 concentrations. However, statistical models have shown inconsistency in PM 2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM 2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NO X , SO 2 , CO, and O 3 ) on the dynamics of PM 2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM 2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO 2 , NO X , and O 3 . Precipitation, relative humidity, and temperature have negative correlations with PM 2.5 . The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM 2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM 2.5 concentrations. This study will help quantify ground-level PM 2.5 concentration exposure and recommend regional government actions to prevent and regulate PM 2.5 air pollution. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01329-w.

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Section: Introductionmentioning

confidence: 99%

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

Islam

Awadh

Mallick

et al. 2023

Air Qual Atmos Health

View full text Add to dashboard Cite

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“…Before introducing the structure for this paper, some other articles applied similar methods to analyze the CO2 emission data. For example, Kumar et al mainly applied time series analysis and machine learning prediction models to analyze and predict future air quality [5]. Since CO 2 is one of the most critical factors, the CO2 emission has also been forecasted using ARIMA and linear regression model [5].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Kumar et al mainly applied time series analysis and machine learning prediction models to analyze and predict future air quality [5]. Since CO 2 is one of the most critical factors, the CO2 emission has also been forecasted using ARIMA and linear regression model [5]. Besides, Nyoni et al have also applied time series models, e.g., the ARIMA prediction model, to forecasting CO 2 emissions in China [6].…”

Section: Introductionmentioning

confidence: 99%

“…The research implemented a method of exhaustion to establish and select ARIMA models. Other scholars studied India's annual CO2 emission data to find the emission trend in the next decade [7]. To achieve this goal, the author not only introduced time series models for forecasting CO2 emissions in the future but also applied some machine learning and deep learning models, e.g., linear regression, random forest regression, and LSTM.…”

Section: Introductionmentioning

confidence: 99%

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The CO2 Emission Forecasting in Asia in Context of Time-series and Machine Learning Approaches

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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Contemporarily, Asia is the world's largest carbon emitter among other continents according to the recorded data, which plays an essential role in climate change. This paper introduces Asia's carbon dioxide emission effect and mainly focuses on the emission prediction for the future five years based on the time series data from 1950 to 2021. Some statistical models are implemented in forecasting, including ARIMA, SARIMA, and SARIMAX models. Besides, two general machine learning models, linear regression and random forest regression would also be applicable in this paper. According to the analysis, based on the model performance matrix, the study found that the SARIMA model is the optimal model to explain and forecast the data. Specifically, it has the lowest MSE, MRSE, and MAE values and the highest AIC and BIC scores. These results shed light on guiding further exploration of carbon dioxide emission prediction in the next five years in Asia.

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“…The resultant findings include that the overall CO2 emissions in India are projected to rise 2915 million tons by 2020. In 2022 [9], Surbhi Kumari and Sunil Kumar Singh addressed the problem for accurate Prediction of CO2 emission in India by applying three models LSTM, SARIMAX, and Holt-Winters model on 40 years of CO2 emission data. They concluded that LSTM is the best performing model for CO2 emission Prediction.…”

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confidence: 99%

A method to mitigate GHG emissions produced from Diwali crackers using Principal Component Analysis

Katuri,

Manda,

et al. 2023

Preprint

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Due to the rapidly expanding population, currently there has been an increase in the use of chemicals that are released into the atmosphere. The primary cause of this is pollution from various sorts of cracker producing enterprises and its bomb products, which, when burned, have a significant negative impact on the atmosphere and cause a wide range of issues for people. The proposed study intends to create a system for evaluating crackers in a certain area based on their capacity and emission values, and applies principal component analysis to identify the crackers that produce the most emissions. The suggested method uses principal component analysis (PCA) to reduce the dimensionality of the dataset and enhance the error factor. From the regional data, Eigenvalues, Variability (%), and Cumulative (%) have been examined. The outcomes can assist the authorities in reducing the air pollution-causing fireworks and subsequently controlling their sale and usage in the area.

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Machine learning-based time series models for effective CO2 emission prediction in India

Cited by 58 publications

References 41 publications

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

The CO2 Emission Forecasting in Asia in Context of Time-series and Machine Learning Approaches

A method to mitigate GHG emissions produced from Diwali crackers using Principal Component Analysis

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