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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators

文献类型： 外文期刊

作者： Jiang, Shenqiong 1 ; Cheng, Xiangju 1 ; Shi, Baoshan 1 ; Zhu, Dantong 1 ; Xie, Jun 3 ; Zhou, Zhihong 4 ;

作者机构： 1.South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Peoples R China

2.South China Univ Technol, State Key Lab Subtrop Bldg & Urban Sci, Guangzhou 510640, Peoples R China

3.Chinese Acad Fishery Sci, Pearl River Fisheries Res Inst, Key Lab Trop & Subtrop Fishery Resource Applicat &, Guangzhou 510380, Peoples R China

4.Guangzhou Ecol & Environm Monitoring Ctr Guangdong, Guangzhou 510030, Peoples R China

关键词： Machine learning model; Water quality prediction; Optimization algorithms; Sensitivity analysis; Ciprofloxacin

期刊名称：ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY （ 影响因子：6.1； 五年影响因子：6.4 ）

ISSN： 0147-6513

年卷期： 2025 年 289 卷

页码：

收录情况： SCI

摘要： The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rapid prediction of antibiotics. Many scholars have shown the effectiveness of machine learning in water quality prediction, however, its effectiveness in predicting antibiotic concentrations in the aquatic environment remains inconclusive. Given that conventional water quality indicators directly or indirectly influence antibiotic concentrations, we explored the feasibility of predicting ciprofloxacin (CFX) concentrations based on conventional water quality indicators with the help of three commonly used machine learning algorithms and two parameter optimization algorithms. Then, we evaluated and determined the best model using four commonly used model performance evaluation metrics. The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. The performance evaluations were R2= 0.936, NSE= 0.915, RMSE= 3.150 ng/L, and MAPE= 30.909 %. Overall, the CFX prediction models met the requirements for antibiotic concentration prediction accuracy, offering a potential indirect method for predicting antibiotic concentrations in water quality management.

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