Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia
Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional pa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3832 |
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| author | Muhammad Muhitur Rahman Md Shafiullah Md Shafiul Alam Mohammad Shahedur Rahman Mohammed Ahmed Alsanad Mohammed Monirul Islam Md Kamrul Islam Syed Masiur Rahman |
| author_facet | Muhammad Muhitur Rahman Md Shafiullah Md Shafiul Alam Mohammad Shahedur Rahman Mohammed Ahmed Alsanad Mohammed Monirul Islam Md Kamrul Islam Syed Masiur Rahman |
| author_sort | Muhammad Muhitur Rahman |
| collection | DOAJ |
| description | Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R<sup>2</sup>) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO<sub>2</sub>e) and mean absolute percentage error (0.29 GtCO<sub>2</sub>e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO<sub>2</sub> equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change. |
| first_indexed | 2024-03-11T06:58:17Z |
| format | Article |
| id | doaj.art-21f59c63fb854daeb4945ffc3adad0c1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:58:17Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-21f59c63fb854daeb4945ffc3adad0c12023-11-17T09:27:33ZengMDPI AGApplied Sciences2076-34172023-03-01136383210.3390/app13063832Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi ArabiaMuhammad Muhitur Rahman0Md Shafiullah1Md Shafiul Alam2Mohammad Shahedur Rahman3Mohammed Ahmed Alsanad4Mohammed Monirul Islam5Md Kamrul Islam6Syed Masiur Rahman7Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaInterdisciplinary Research Center for Renewable Energy and Power Systems, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaApplied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaCivil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi ArabiaDepartment of Environment and Agricultural Natural Resources, College of Agricultural and Food Sciences, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Biomedical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaApplied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaGreenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R<sup>2</sup>) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO<sub>2</sub>e) and mean absolute percentage error (0.29 GtCO<sub>2</sub>e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO<sub>2</sub> equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.https://www.mdpi.com/2076-3417/13/6/3832machine learningbagged decision treeboosted decision treegradient boosted decision treegreenhouse gas emission |
| spellingShingle | Muhammad Muhitur Rahman Md Shafiullah Md Shafiul Alam Mohammad Shahedur Rahman Mohammed Ahmed Alsanad Mohammed Monirul Islam Md Kamrul Islam Syed Masiur Rahman Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia Applied Sciences machine learning bagged decision tree boosted decision tree gradient boosted decision tree greenhouse gas emission |
| title | Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia |
| title_full | Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia |
| title_fullStr | Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia |
| title_full_unstemmed | Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia |
| title_short | Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia |
| title_sort | decision tree based ensemble model for predicting national greenhouse gas emissions in saudi arabia |
| topic | machine learning bagged decision tree boosted decision tree gradient boosted decision tree greenhouse gas emission |
| url | https://www.mdpi.com/2076-3417/13/6/3832 |
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