Applicability of machine learning models for the assessment of long-term pollutant leaching from solid waste materials
Copyright © 2023 Elsevier Ltd. All rights reserved.
| Veröffentlicht in: | Waste management (New York, N.Y.). - 1999. - 171(2023) vom: 10. Sept., Seite 337-349 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Waste management (New York, N.Y.) |
| Schlagworte: | Journal Article Column leaching test Construction and demolition waste Liquid to solid ratio Machine learning PAHs |
| Zusammenfassung: | Copyright © 2023 Elsevier Ltd. All rights reserved. Column leaching tests are a common approach for evaluating the leaching behavior of contaminated soil and waste materials, which are often reused for various construction purposes. Standardized up-flow column leaching tests typically require about 7 days of laboratory work to evaluate long-term leaching behavior accurately. To reduce testing time, we developed linear and ensemble models based on parametric and non-parametric Machine Learning (ML) techniques. These models predict leachate concentrations of relevant chemical compounds at different Liquid-to-Solid ratios (LS) based on measurements at lower LS values. The ML models were trained using 82 column leaching test samples for Construction and Demolition Waste materials collected in Germany during the last two decades. R-Squared values measuring models' performance are as follows: Sulfate = 0.94, Vanadium = 0.97, Chromium = 0.82, Copper = 0.92, group of 15 (US-EPA) PAHs = 0.98 (values averaged over predictive models for LS 2 and 4). Sensitivity analysis utilizing the Shapley Additive Explanation value indicates that in addition to the concentrations of the considered compound at LS<=1, electrical conductivity and pH are the most critical features of each model, while concentrations of other compounds also play a minor role |
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| Beschreibung: | Date Revised 12.09.2023 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2023.09.001 |