Article ; Online: Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study.
2024 Volume 246, Page(s) 118146
Abstract: Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in ... ...
| Abstract | Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed. This study aims to investigate the impact of six FS methods (categorized as Wrapper, Filter, and Embedded methods) on the accuracy of three supervised ML algorithms in predicting total suspended solids (TSS) concentration in the effluent of a municipal wastewater treatment plant. Based on the features proposed by each FS method, five distinct scenarios were defined. Within each scenario, three ML algorithms, namely artificial neural network-multi layer perceptron (ANN-MLP), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost) were applied. The features utilized for predicting TSS concentration in the WWTP effluent included BOD |
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| MeSH term(s) | Waste Disposal, Fluid/methods ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Water Purification/methods |
| Language | English |
| Publishing date | 2024-01-11 |
| Publishing country | Netherlands |
| Document type | Journal Article |
| ZDB-ID | 205699-9 |
| ISSN | 1096-0953 ; 0013-9351 |
| ISSN (online) | 1096-0953 |
| ISSN | 0013-9351 |
| DOI | 10.1016/j.envres.2024.118146 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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