Indirect models for SWCC parameters: reducing prediction uncertainty with machine learning

He, X; Cai, G; Sheng, D

Indirect models for SWCC parameters: reducing prediction uncertainty with machine learning

He, X

Cai, G Sheng, D

Permalink

Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Computers and Geotechnics, 2025, 177
Issue Date:: 2025-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (14.21 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Cai, G
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2025-07-16T05:16:43Z
dc.date.available	2025-07-16T05:16:43Z
dc.date.issued	2025-01-01
dc.identifier.citation	Computers and Geotechnics, 2025, 177
dc.identifier.issn	0266-352X
dc.identifier.issn	1873-7633
dc.identifier.uri	http://hdl.handle.net/10453/188431
dc.description.abstract	The soil–water characteristic curve (SWCC) is crucial for modelling the transport of water and hazardous materials in the vadose zone. However, measuring SWCC is often cumbersome and time-consuming. This paper introduces indirect models that predict SWCC parameters in probabilistic distributions using easily measurable quantities such as particle-size distributions and porosity. This paper starts with building a joint normal model and the derived conditional probability from it serves as a predictive model. However, this model had extremely high prediction uncertainty. To reduce such uncertainty, various machine-learning techniques were explored, including introducing the dependence of variation scale on predictors, using artificial neural networks (ANN) to model nonlinear dependence, incorporating additional predictive features, and generating a larger dataset. The final machine-learning model successfully reduces prediction variability and has been rigorously tested on a separate set of samples to prevent overfitting.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Computers and Geotechnics
dc.relation.isbasedon	10.1016/j.compgeo.2024.106823
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4019 Resources engineering and extractive metallurgy
dc.title	Indirect models for SWCC parameters: reducing prediction uncertainty with machine learning
dc.type	Journal Article
utslib.citation.volume	177
utslib.for	0905 Civil Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-16T05:16:29Z
pubs.publication-status	Published
pubs.volume	177

Abstract:

The soil–water characteristic curve (SWCC) is crucial for modelling the transport of water and hazardous materials in the vadose zone. However, measuring SWCC is often cumbersome and time-consuming. This paper introduces indirect models that predict SWCC parameters in probabilistic distributions using easily measurable quantities such as particle-size distributions and porosity. This paper starts with building a joint normal model and the derived conditional probability from it serves as a predictive model. However, this model had extremely high prediction uncertainty. To reduce such uncertainty, various machine-learning techniques were explored, including introducing the dependence of variation scale on predictors, using artificial neural networks (ANN) to model nonlinear dependence, incorporating additional predictive features, and generating a larger dataset. The final machine-learning model successfully reduces prediction variability and has been rigorously tested on a separate set of samples to prevent overfitting.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/188431