[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119841-en":3,"doc-seo-119841-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119841,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques","Carbon dioxide (CO2) emissions are a growing global concern due to climate change impacts such as higher temperatures and rising sea levels. Thailand’s rapid industrialization, population growth, and increasing energy demand have accelerated CO2 emissions, creating the need for reliable forecasting to support sustainable development. This study applies multiple machine learning models—Random Forest, Gradient Boosting Regression, XGBoost, SVC, Decision Trees, KNN, PCA, ensemble methods, and Genetic Algorithms—then evaluates performance using R2, MAE, RMSE, MAPE, and correctness. Results compare model strengths and weaknesses to inform policy-relevant emission reduction planning.","Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques  \nSiriporn Chimphlee1, Witcha Chimphlee2  \n1,2Faculty of Sciene and Technology, Suan Dusit University, Thailand  \nArticle history:  \nReceived May 30, 2023 Revised Aug 28, 2023 Accepted Sep 23, 2023  \nKeywords:  \nThailand  \nCO2 emission Carbon dioxide Forecasting Machine Learning  \nCorresponding Author:  \nWitcha Chimphlee,  \nFaculty of Science and Technology, Suan Dusit University,  \nMachine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trendsand identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including Rsquared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness. The results show that the RF and XGB algorithms function exceptionally well, with high Rsquared values and low error rates. KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower Rsquared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.  \nCopyright © 2023 Institute of Advanced Engineering and Science.  \nAll rights reserved.  \n295 Ratchasima Road, Dusit, Bangkok 10300, Thailand. [Email: witcha_chi@dusit.ac.th](Email: witcha_chi@dusit.ac.th)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSince global warning has developed into a problem that affects climate change and the environment [1][2][3]–[5] and cause the greenhouse effect [6], which raises global temperatures, causes sea levels to rise, and has detrimental consequences on ecosystems and public health. CO2 emissions have therefore become a global problem. This issue of greenhouse gases and increasing energy use has led to interest in predicting future carbon dioxide emissions. Like many other nations, Thailand struggles to successfully manage and lower CO2 emissions. The nation's CO2 emissions have significantly increased as a result of the quick industrialization, rising population, and rising energy needs [7] . To reduce the negative effects on the environment and encourage sustainable growth, it is imperative to address this issue and create effective systems for tracking, forecasting, and reducing CO2 emissions. Due to increased CO2 emissions [8], Thailand confronts serious environmental challenges [9][10] . Accurate emission forecasting can help stakeholders, academics, and policymakers create effective plans to reduce negative environmental effects and advance sustainable development. Machine learning algorithms provide an effective collection of tools for examining huge datasets, finding patterns, and producing accurate predictions [11][12]–[15] .  \nAccurately forecasting carbon dioxide (CO2) emissions has become a crucial problem as concern over climate change and its effects on the environment has grown. It has been demonstrated that machine learning [6], [7], [16]–[19] approaches are useful for modeling complex relationships and producing precise  \npredictions. Using a variety of machine learning algorithms [7], such as Random Forest (RF) [19], Gradient Boosting Regression (GBR) , XGBoost (XGB), Support Vector Machines (SVC) [20][21], Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component ","cbCaisZMI7kvXWhY","https://ap.wps.com/l/cbCaisZMI7kvXWhY","pdf",809510,1,15,"English","en",105,"# Introduction\n## Problem background and motivation\n## Research aims and gaps\n# Machine Learning Methods\n## Models used\n## Feature processing and dimensionality reduction\n# Model Evaluation\n## Metrics and correctness\n# Results and Discussion\n## Comparative performance of algorithms\n# Conclusion\n## Implications for sustainable policy","[{\"question\":\"Which machine learning models are used to forecast CO2 emissions in Thailand?\",\"answer\":\"The study uses Random Forest, Gradient Boosting Regression, XGBoost, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Principal Component Analysis, ensemble methods, and Genetic Algorithms.\"},{\"question\":\"How is model performance evaluated in the paper?\",\"answer\":\"Performance is measured using R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and a correctness criterion.\"},{\"question\":\"What do the results indicate about the best-performing algorithms?\",\"answer\":\"Random Forest and XGBoost achieve exceptionally strong performance with high R2 and low error rates, while KNN, PCA, ensemble methods, and Genetic Algorithms show higher errors and lower R2, suggesting weaker prediction accuracy.\"}]","Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques | 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machine learning models are used to forecast CO2 emissions in Thailand?","Question",{"text":75,"@type":76},"The study uses Random Forest, Gradient Boosting Regression, XGBoost, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Principal Component Analysis, ensemble methods, and Genetic Algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is model performance evaluated in the paper?",{"text":80,"@type":76},"Performance is measured using R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and a correctness criterion.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about the best-performing algorithms?",{"text":84,"@type":76},"Random Forest and XGBoost achieve exceptionally strong performance with high R2 and low error rates, while KNN, PCA, ensemble methods, and Genetic Algorithms show higher errors and lower R2, suggesting weaker prediction 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