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Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data
This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated s...
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| Published in: | Applied sciences 2024-11, Vol.14 (22), p.10689 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c255t-512d232e543f3966161dfe94d94dd8e858fdcb6aa77742527bd6161df729eff93 |
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| container_issue | 22 |
| container_start_page | 10689 |
| container_title | Applied sciences |
| container_volume | 14 |
| creator | Gulshin, Igor Kuzina, Olga |
| description | This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated sludge. The feasibility of using data obtained under laboratory conditions and simulating the technological process as a training dataset is explored. A small dataset collected from actual wastewater treatment plants is considered as the test dataset. For both regression and classification tasks, the best results were achieved using gradient-boosting models from the CatBoost family, yielding metrics of SMAPE = 9.1 and ROC-AUC = 1.0. A set of the most important predictors for modeling was selected for each of the target features. |
| doi_str_mv | 10.3390/app142210689 |
| format | article |
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| ispartof | Applied sciences, 2024-11, Vol.14 (22), p.10689 |
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| language | eng |
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| source | Publicly Available Content Database |
| subjects | Algorithms Data collection effluent quality Energy consumption Machine learning machine learning algorithms Methods Neural networks Nitrogen Purification Sensors Sewage Sludge soft sensors Time series wastewater treatment Water treatment Water treatment plants Water utilities |
| title | Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data |
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