Skip to main content

Machine learning models for predicting PAHs bioavailability in compost amended soils

Wu, Guozhong, Kechavarzi, Cedric, Li, Xingang, Wu, Shaomin, Pollard, Simon J. T., Sui, Hong, Coulon, Frederic (2013) Machine learning models for predicting PAHs bioavailability in compost amended soils. Chemical Engineering Journal, 223 . pp. 747-754. ISSN 1385-8947. (doi:10.1016/j.cej.2013.02.122) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:35146)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.cej.2013.02.122

Abstract

Compost addition to polluted soils is a strategy for waste reuse and soil remediation, while bioavailability is a key parameter for environmental assessment. Empirical data from an 8-month microcosm experiment were used to assess the ability and performance of six machine learning (ML) models to predict temporal bioavailability changes of 16 polycyclic aromatic hydrocarbons (PAHs) in contaminated soils amended with compost. The models included multilayer perceptrons (MLPs), radial basis function (RBF), support vector regression (SVR), M5 model tree (M5P), M5 rule (M5R) and linear regression (LR). Overall, the performance of the six models, determined by 10-fold cross validation method, was ranked as follows: RBF > M5P > SVR > MLP > M5R > LR. Results further demonstrated that the ML models successfully identified the relative importance of each variable (i.e. incubation time, organic carbon content, soil moisture content, nutrient levels) on the temporal bioavailability change of individual PAH. Such models can potentially be useful for predicting the concentration of a wide range of pollutants in soils, which could contribute to reduce chemical monitoring at site and help decision making for remediation end points and risk assessment.

Item Type: Article
DOI/Identification number: 10.1016/j.cej.2013.02.122
Uncontrolled keywords: Machine Learning; Bioavailability; Compost; PAH; Cyclodextrin extraction
Subjects: H Social Sciences
H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Shaomin Wu
Date Deposited: 09 Sep 2013 12:03 UTC
Last Modified: 16 Feb 2021 12:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35146 (The current URI for this page, for reference purposes)
Wu, Shaomin: https://orcid.org/0000-0001-9786-3213
  • Depositors only (login required):