Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators
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 s...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324017044 |
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| author | Shenqiong Jiang Xiangju Cheng Baoshan Shi Dantong Zhu Jun Xie Zhihong Zhou |
| author_facet | Shenqiong Jiang Xiangju Cheng Baoshan Shi Dantong Zhu Jun Xie Zhihong Zhou |
| author_sort | Shenqiong Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8f3da1440dca4a77926af126f51915d3 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-8f3da1440dca4a77926af126f51915d32025-01-23T05:25:54ZengElsevierEcotoxicology and Environmental Safety0147-65132025-01-01289117628Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicatorsShenqiong Jiang0Xiangju Cheng1Baoshan Shi2Dantong Zhu3Jun Xie4Zhihong Zhou5School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China; Corresponding author at: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, ChinaKey Laboratory of Tropical and Subtropical Fishery Resource Application and Cultivation, Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaGuangzhou Ecological and Environmental Monitoring Center of Guangdong Province, Guangzhou 510030, ChinaThe 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.http://www.sciencedirect.com/science/article/pii/S0147651324017044Machine learning modelWater quality predictionOptimization algorithmsSensitivity analysisCiprofloxacin |
| spellingShingle | Shenqiong Jiang Xiangju Cheng Baoshan Shi Dantong Zhu Jun Xie Zhihong Zhou Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators Ecotoxicology and Environmental Safety Machine learning model Water quality prediction Optimization algorithms Sensitivity analysis Ciprofloxacin |
| title | Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| title_full | Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| title_fullStr | Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| title_full_unstemmed | Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| title_short | Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| title_sort | optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators |
| topic | Machine learning model Water quality prediction Optimization algorithms Sensitivity analysis Ciprofloxacin |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324017044 |
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