Estimation of transport CO2 emissions using machine learning algorithm

Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: 133, P. 104276 - 104276

Published: June 5, 2024

Language: Английский

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Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban PM2.5 concentration prediction of India’s polluted cities

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141259 - 141259

Published: Feb. 14, 2024

Language: Английский

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An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract Human-induced global warming, primarily attributed to the rise in atmospheric CO 2 , poses a substantial risk survival of humanity. While most research focuses on predicting annual emissions, which are crucial for setting long-term emission mitigation targets, precise prediction daily emissions is equally vital short-term targets. This study examines performance 14 models data from 1/1/2022 30/9/2023 across top four polluting regions (China, India, USA, and EU27&UK). The used include statistical (ARMA, ARIMA, SARMA, SARIMA), three machine learning (support vector (SVM), random forest (RF), gradient boosting (GB)), seven deep (artificial neural network (ANN), recurrent variations such as gated unit (GRU), long memory (LSTM), bidirectional-LSTM (BILSTM), hybrid combinations CNN-RNN). Performance evaluation employs metrics ( R MAE, RMSE, MAPE). results show that (ML) (DL) models, with higher (0.714–0.932) lower RMSE (0.480–0.247) values, respectively, outperformed model, had (− 0.060–0.719) (1.695–0.537) all regions. ML DL was further enhanced by differencing, technique improves accuracy ensuring stationarity creating additional features patterns model can learn. Additionally, applying ensemble techniques bagging voting improved approximately 9.6%, whereas CNN-RNN RNN models. In summary, both relatively similar. However, due high computational requirements associated recommended using bagging. assist accurately forecasting aiding authorities targets reduction.

Language: Английский

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The Interrelation among Environmental Quality, Public Accounts, and Macroeconomic Fundamentals: An Analysis of OECD Countries Using Machine Learning Techniques

Environmental Development, Journal Year: 2025, Volume and Issue: unknown, P. 101175 - 101175

Published: Feb. 1, 2025

Language: Английский

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Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 122, P. 106157 - 106157

Published: March 16, 2023

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak sickness 2019. Corona Virus Disease 2019 (COVID-19) is name that World Health Organization (WHO) officially gave to pneumonia caused by on February 11, 2020. The use models informed machine learning currently a major focus study field improved forecasting. By displaying annual trends, forecasting can be performing impact assessments potential outcomes. In this paper, proposed forecast consisting time series such as long short-term memory (LSTM), bidirectional (Bi-LSTM), generalized regression unit (GRU), dense-LSTM have been evaluated prediction confirmed cases, deaths, recoveries 12 countries affected COVID-19. Tensorflow1.0 used programming. Indices known mean absolute error (MAE), root means square (RMSE), Median Absolute Error (MEDAE) r2 score utilized process evaluating performance models. We presented various ways time-series making LSTM (LSTM, BiLSTM), we compared these methods other evaluate Our suggests based among most advanced data.

Language: Английский

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A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(55), P. 117485 - 117502

Published: Oct. 23, 2023

Language: Английский

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

Air Quality Atmosphere & Health, Journal Year: 2023, Volume and Issue: 16(6), P. 1117 - 1139

Published: Feb. 25, 2023

Language: Английский

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Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4219 - 4219

Published: May 17, 2024

Under the Paris Agreement, countries must articulate their most ambitious mitigation targets in Nationally Determined Contributions (NDCs) every five years and regularly submit interconnected information on greenhouse gas (GHG) aspects, including national GHG inventories, NDC progress tracking, policies measures (PAMs), projections various scenarios. Research highlights significant gaps definition of reporting GHG-related elements, such as inconsistencies between projections, targets, a disconnect PAMs scenarios, well varied methodological approaches across sectors. To address these challenges, Mitigation-Inventory Tool for Integrated Climate Action (MITICA) provides framework that links applying hybrid decomposition approach integrates machine learning regression techniques with classical forecasting methods developing emission projections. MITICA enables scenario generation until 2050, incorporating over 60 Intergovernmental Panel Change (IPCC) It is first modelling ensures consistency aligning tracking target setting IPCC best practices while linking climate change sustainable economic development. MITICA’s results include align observed trends, validated through cross-validation against test data, employ robust evaluating PAMs, thereby establishing its reliability.

Language: Английский

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Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33148 - e33148

Published: June 20, 2024

Carbon dioxide (CO

Language: Английский

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Estimating fossil CO2 emissions from COVID-19 post-pandemic recovery in G20: A machine learning approach

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 442, P. 140875 - 140875

Published: Jan. 26, 2024

Language: Английский

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