Combination of effective color information and machine learning for rapid prediction of soil water content
文献类型：期刊论文
| 作者 | Liu, Guanshi1; Tian, Shengkui1,2; Xu, Guofang1; Zhang, Chengcheng1,2; Cai, Mingxuan1,2 |
| 刊名 | JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:9页码:2441-2457 |
| 关键词 | Soil water content (SWC) Digital image Soil color Color space Machine learning |
| ISSN号 | 1674-7755 |
| DOI | 10.1016/j.jrmge.2022.12.029 |
| 英文摘要 | Soil water content (SWC) is one of the critical indicators in various fields such as geotechnical engineering and agriculture. To avoid the time-consuming, destructive, and laborious drawbacks of conventional SWC measurements, the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis. In this study, a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images, aiming to alleviate the interference of complex surface conditions with color information extraction. In addition, an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images. Meanwhile, a subset of 10 parameters is identified through three variable analytical methods. Then, four machine learning models for SWC prediction based on partial least squares regression (PLSR), random forest (RF), support vector machines regression (SVMR), and Gaussian process regression (GPR), are established using 32 and 10 characteristic parameters, and their performance is compared. The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions. The RGB, CIEXYZ, and CIELCH color spaces and lightness parameters, as the inputs, are more suitable for the SWC prediction models. Furthermore, it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy, and the GPR model has the best prediction performance (R-2 +/- 0.95, RMSE <= 2.01%, RPD >= 4.95, and RPIQ >= 6.37). The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction, providing valuable insights for mapping soil moisture fields. (c) 2023 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). |
| 资助项目 | National Natural Science Foundation of China[52179115] ; National Natural Science Foundation of China[52178372] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001070906500018 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/39538]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tian, Shengkui |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 2.Guilin Univ Technol, Guangxi Key Lab Rock & Soil Mech & Engn, Guilin 541004, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Guanshi,Tian, Shengkui,Xu, Guofang,et al. Combination of effective color information and machine learning for rapid prediction of soil water content[J]. JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,2023,15(9):2441-2457. |
| APA | Liu, Guanshi,Tian, Shengkui,Xu, Guofang,Zhang, Chengcheng,&Cai, Mingxuan.(2023).Combination of effective color information and machine learning for rapid prediction of soil water content.JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,15(9),2441-2457. |
| MLA | Liu, Guanshi,et al."Combination of effective color information and machine learning for rapid prediction of soil water content".JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 15.9(2023):2441-2457. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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