Physics-guided genetic programming for predicting field-monitored suction variation with effects of vegetation and atmosphere
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{CHENG:2023:enggeo,
-
author = "Zhi-Liang Cheng and K. K. Pabodha M. Kannangara and
Li-Jun Su and Wan-Huan Zhou and Chen Tian",
-
title = "Physics-guided genetic programming for predicting
field-monitored suction variation with effects of
vegetation and atmosphere",
-
journal = "Engineering Geology",
-
volume = "315",
-
pages = "107031",
-
year = "2023",
-
ISSN = "0013-7952",
-
DOI = "doi:10.1016/j.enggeo.2023.107031",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0013795223000480",
-
keywords = "genetic algorithms, genetic programming,
Field-monitored soil suction, Physics-guided genetic
programming, Performance evaluation, Global sensitivity
analysis, Uncertainty analysis",
-
abstract = "The complicated interactions among shallow soil,
vegetation, and atmospheric parameters make the precise
prediction of field-monitored soil suction under
natural conditions challenging. This study integrated
an analytical solution with a genetic programming (GP)
model in proposing a physics-guided GP method for
better calculation and prediction of field-monitored
matric suction in a shallow soil layer. Model
development and analysis involved 3987 collected data
values for soil suction as well as atmospheric and
tree-related parameters from a field monitoring site.
Natural algorithm values of transpiration rates
obtained by back-calculation were simulated with GP
using easily obtained parameters. Global sensitivity
analysis demonstrated that the tree canopy-related
parameter was the most important for transpiration
rate. It was indicated that the proposed physics-guided
GP method greatly improved calculation accuracy and, as
a result, demonstrated a better performance and was
more reliable than the individual GP method in
calculating field-monitored suction. The proposed
physics-guided GP method was also validated as more
stable and reliable due to its smaller uncertainty and
higher confidence level compared to the individual GP
method based on quantile regression uncertainty
analysis",
- }
Genetic Programming entries for
Zhi-Liang Cheng
K K Pabodha M Kannangara
Li-Jun Su
Wan-Huan (Hanna) Zhou
Chen Tian
Citations