Predicting the hydraulic conductivity of compacted soil barriers in landfills using machine learning techniques
Document Type
Article
Publication Date
2-15-2023
Subject Area
Soil; Waste Disposal Facilities
Abstract
Machine learning models (MLMs) were developed to predict saturated hydraulic conductivity of compacted soil barriers and help to identify appropriate soils for the construction of landfill liners and covers. Data from hydraulic conductivity tests on compacted soil barriers were collected from the literature and compiled into a database for MLM construction. The database contains 329 records of hydraulic conductivity tests associated with 12 selected impact factors covering physical properties, compaction efforts, and hydration and mineralogy behaviors of compacted soil barriers. Three machine learning algorithms (random forest, gradient boosting decision tree, and neural network) were used to develop MLMs, and a statistical technique (multiple linear regression) was used to compare the precision of predictions with the MLMs. Results from this study showed that the random forest model provided the best prediction of the hydraulic conductivity of compacted soil barriers, with 100% of predicted hydraulic conductivity within 100-time differences to measured hydraulic conductivity and 93% within 10-time differences. Feature importance analysis showed that void ratio after compaction, fines content, specific gravity, degree of saturation after compaction, and plasticity index of soils are the top-five factors (in descending order) that influence the hydraulic conductivity of compacted soil barriers and are recommended for a precise prediction. Three predictive MLMs were created for industries as simple tools to screen the soils prior to the construction of compacted soil barriers in landfill liners and covers.
Publication Title
Waste management (New York, N.Y.)
Volume
157
First Page
357
Last Page
366
Digital Object Identifier (DOI)
10.1016/j.wasman.2023.01.003
PubMed ID
36630884
E-ISSN
1879-2456
Language
eng
Citation Information
Tan, Yu; Zhang, Poyu; Chen, Jiannan; Shamet, Ryan; Hyun Nam, Boo; and Pu, Hefu, "Predicting the hydraulic conductivity of compacted soil barriers in landfills using machine learning techniques" (2023). UNF Faculty Research and Scholarship. 3212.
https://digitalcommons.unf.edu/unf_faculty_publications/3212