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Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应
Cheng,Zhi liang1; Zhou,Wan huan1,2; Ding,Zhi1,3; Guo,Yong xing1,4
2020-06-01
Source PublicationJournal of Zhejiang University: Science A
ISSN1673-565X
Volume21Issue:6Pages:462-477
Abstract

In this study, a machine learning method, i.e. genetic programming (GP), is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters. The data used for model development was recorded by an in-situ experiment. The image processing technology is used to quantify several tree canopy parameters. Based on four accuracy metrics, i.e. root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R), and relative error, the performance of the proposed GP model was evaluated. The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors. Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations. A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.

KeywordGenetic Programming (Gp) Simplified Statistical Model Soil Suction Spatiotemporal Variations Tu413.7
DOI10.1631/jzus.A1900555
URLView the original
Indexed BySCIE
Language中文Chinese
WOS Research AreaEngineering ; Physics
WOS SubjectEngineering, Multidisciplinary ; Physics, Applied
WOS IDWOS:000540539800005
Scopus ID2-s2.0-85086506750
Fulltext Access
FWCI1.5177201
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorZhou,Wan huan
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,999078,Macao
2.Zhuhai UM Science and Technology Research Institute,Zhuhai,519000,China
3.Department of Civil and Environmental Engineering,School of Engineering,Zhejiang University City College,Hangzhou,310015,China
4.Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan,430081,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Cheng,Zhi liang,Zhou,Wan huan,Ding,Zhi,等. Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应[J]. Journal of Zhejiang University: Science A,2020,21(6):462-477.
APA Cheng,Zhi liang,Zhou,Wan huan,Ding,Zhi,&Guo,Yong xing.(2020).Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应.Journal of Zhejiang University: Science A,21(6),462-477.
MLA Cheng,Zhi liang,et al."Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应".Journal of Zhejiang University: Science A 21.6(2020):462-477.
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