@article{ab23e3211f914afc9c4f7aee5ca10498,
title = "Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)",
abstract = "The Gaussian Process Regression (GPR) model, from machine learning algorithm, exhibited high generalization and adequacy in correlating the photocatalytic degradation of POME with its operating conditions.",
keywords = "Generalization enhancement, Machine learning predictive models, Palm oil mill effluent, Photocatalytic treatment",
author = "Ng, {Kim Hoong} and Gan, {Y. S.} and Cheng, {Chin Kui} and Liu, {Kun Hong} and Liong, {Sze Teng}",
note = "Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2, MOST 108-2218-E-009-054-MY2, MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002-.), National Natural Science Foundation of China (No. 61772023) and National Key R&D Program of China (No. 2019QY1803). Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2 , MOST 108-2218-E-009-054-MY2 , MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002- .), National Natural Science Foundation of China (No. 61772023 ) and National Key R&D Program of China (No. 2019QY1803 ). Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2020",
month = dec,
doi = "10.1016/j.envpol.2020.115500",
language = "British English",
volume = "267",
journal = "Environmental Pollution",
issn = "0269-7491",
publisher = "Elsevier",
}