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Bao H, Yin W, Wang H, Lu Y, Jiang S, Ajibade FO, Ouyang Q, Wang Y, Nie S, Bai Y, Gao H, Wang A. Automated machine learning-based models for predicting and evaluating antibiotic removal in constructed wetlands. Bioresour Technol 2023; 385:129436. [PMID: 37399962 DOI: 10.1016/j.biortech.2023.129436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/19/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023]
Abstract
Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94-13.68, coefficient of determination = 0.780-0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.
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Affiliation(s)
- Hongxu Bao
- College of the Environment, Liaoning University, Shenyang 110036, China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Wanxin Yin
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Hongcheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
| | - Yin Lu
- College of Environment and Surveying and Mapping, China University of Mining and Technology, Xuzhou 221116, China
| | - Shijie Jiang
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Fidelis Odedishemi Ajibade
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qinghua Ouyang
- Shenshui Hynar Water Group Co., Ltd., Shenzhen 518055, China
| | - Yongji Wang
- Shenshui Hynar Water Group Co., Ltd., Shenzhen 518055, China
| | - Shichen Nie
- Shandong Hynar Water Environmental Protection Co., Ltd., Caoxian, China
| | - Yu Bai
- Unicom Digital Technology Co. Ltd., Beijing 100032, China
| | - Huiliang Gao
- Shenyang Water Group Co., Ltd, Shenyang 110036 China
| | - Aijie Wang
- College of the Environment, Liaoning University, Shenyang 110036, China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China; CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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