Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil
文献类型：期刊论文
| 作者 | Onyekwena, Chikezie Chimere1,2; Xue, Qiang1,2 ; Li, Qi1,2; Wan, Yong1,2; Feng, Song4; Umeobi, Happiness Ijeoma1,2,3; Liu, Hongwei5; Chen, Bowen1,2
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| 刊名 | APPLIED SOFT COMPUTING
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| 出版日期 | 2022-09-01 |
| 卷号 | 127期号:-页码:- |
| 关键词 | Biochar Support vector regression Machine learning Gas diffusion coefficient Degree of compaction Greenhouse gas emission |
| ISSN号 | 1568-4946 |
| 英文摘要 | Measurement of gas diffusion coefficient (Dp) of biochar-amended soil (BAS) under varying conditions is essential for assessing the adsorption capacity and water/gas diffusion in compacted BAS. However, there is no established equation of Dp available on this topic. Also, the factors influencing gas diffusion in BAS have not been properly studied and remain unclear. Various machine learning models were employed in this paper to learn and predict the Dp of BAS based on experimental data. Six factors (i.e., degree of compaction (DOC), biochar content (BC), soil air content (SAC), gravimetric water content (GWC), degree of saturation (DS), and porosity) are considered for testing the prediction models. The epsilon radial basis function support vector regression model showed better accuracy and predictive performance (R = 0.9925) than other models and was further improved by applying the feature selection technique using the multiple linear regression and tree-based models (R = 0.9937). The results reveal that SAC, DS, and porosity are the main predictor variables. The SAC proved to be the most influential predictor variable based on the estimated p-value. Furthermore, the optimal Dp was established for the various DOC and BC, which could be useful in designing engineered landfill covers. The accurate model prediction and relative importance of the predictor variables could significantly minimize the experimental work volume required to determine Dp, thereby saving time and cost.(c) 2022 Elsevier B.V. All rights reserved. |
| 学科主题 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000861101400016 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35278]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Nnamdi Azikiwe University, Awka, Nigeria 4.College of Civil Engineering, Fuzhou University, Fuzhou, China 5.College of Environment and Resource, Fuzhou University, Fuzhou, China |
| 推荐引用方式 GB/T 7714 | Onyekwena, Chikezie Chimere,Xue, Qiang,Li, Qi,et al. Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil[J]. APPLIED SOFT COMPUTING,2022,127(-):-. |
| APA | Onyekwena, Chikezie Chimere.,Xue, Qiang.,Li, Qi.,Wan, Yong.,Feng, Song.,...&Chen, Bowen.(2022).Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil.APPLIED SOFT COMPUTING,127(-),-. |
| MLA | Onyekwena, Chikezie Chimere,et al."Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil".APPLIED SOFT COMPUTING 127.-(2022):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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