Full Text:   <2544>

Summary:  <1783>

CLC number: TU413.7

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2020-05-23

Cited: 0

Clicked: 3811

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhi-liang Cheng

https://orcid.org/0000-0002-0607-8912

Wan-huan Zhou

https://orcid.org/0000-0001-5183-9947

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.462-477

http://doi.org/10.1631/jzus.A1900555


Estimation of spatiotemporal response of rooted soil using a machine learning approach


Author(s):  Zhi-liang Cheng, Wan-huan Zhou, Zhi Ding, Yong-xing Guo

Affiliation(s):  State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau SAR 999078, China; more

Corresponding email(s):   hannahzhou@um.edu.mo

Key Words:  Genetic programming (GP), Simplified statistical model, Spatiotemporal variations, Soil suction



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 (R2), 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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE