Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents
Abstract A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using...
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| Format: | Article |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34963-6 |
| _version_ | 1797827505767317504 |
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| author | Temitope C. Ekundayo Mary A. Adewoyin Oluwatosin A. Ijabadeniyi Etinosa O. Igbinosa Anthony I. Okoh |
| author_facet | Temitope C. Ekundayo Mary A. Adewoyin Oluwatosin A. Ijabadeniyi Etinosa O. Igbinosa Anthony I. Okoh |
| author_sort | Temitope C. Ekundayo |
| collection | DOAJ |
| description | Abstract A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models’ performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040–4.5828)] and Cubist [3.1736 (1.1012–4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00–83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes. |
| first_indexed | 2024-04-09T12:49:24Z |
| format | Article |
| id | doaj.art-63394f45312a47528096ed584a9b8993 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T12:49:24Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-63394f45312a47528096ed584a9b89932023-05-14T11:16:22ZengNature PortfolioScientific Reports2045-23222023-05-0113111410.1038/s41598-023-34963-6Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluentsTemitope C. Ekundayo0Mary A. Adewoyin1Oluwatosin A. Ijabadeniyi2Etinosa O. Igbinosa3Anthony I. Okoh4SAMRC Microbial Water Quality Monitoring Centre, University of Fort HareSAMRC Microbial Water Quality Monitoring Centre, University of Fort HareDepartment of Biotechnology and Food Science, Durban University of TechnologySAMRC Microbial Water Quality Monitoring Centre, University of Fort HareSAMRC Microbial Water Quality Monitoring Centre, University of Fort HareAbstract A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models’ performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040–4.5828)] and Cubist [3.1736 (1.1012–4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00–83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.https://doi.org/10.1038/s41598-023-34963-6 |
| spellingShingle | Temitope C. Ekundayo Mary A. Adewoyin Oluwatosin A. Ijabadeniyi Etinosa O. Igbinosa Anthony I. Okoh Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents Scientific Reports |
| title | Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| title_full | Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| title_fullStr | Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| title_full_unstemmed | Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| title_short | Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| title_sort | machine learning guided determination of acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents |
| url | https://doi.org/10.1038/s41598-023-34963-6 |
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