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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19551315 |
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| _version_ | 1866901967097298944 |
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| author | Zanoni, Maria Grazia |
| author_facet | Zanoni, Maria Grazia |
| contents | <p>This software implements a feed-forward Deep Neural Network (DNN) for estimating groundwater contaminant transport parameters from breakthrough curve descriptors. The model predicts key quantities such as peak concentration (<em>Cpeak</em>), time to peak (<em>Tpeak</em>), and the second temporal moment (<em>S2tau</em>).</p> <p>The network is trained using synthetic data and evaluated through 5-fold cross-validation based on the Nash–Sutcliffe efficiency. The repository includes the workflow required to reproduce the DNN inference results presented in the associated study: Zanoni, M. G., de Barros, F. P.J., Bonazzi, A., and Bellin, A., <em>Enhancing Groundwater Contaminant Transport Forecasts Through Synergy of Machine Learning and Physics-Based Models</em>, submitted to Water Resources Research, 2026</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19551315 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Deep neural network model for estimating transport parameters from breakthrough curves Zanoni, Maria Grazia <p>This software implements a feed-forward Deep Neural Network (DNN) for estimating groundwater contaminant transport parameters from breakthrough curve descriptors. The model predicts key quantities such as peak concentration (<em>Cpeak</em>), time to peak (<em>Tpeak</em>), and the second temporal moment (<em>S2tau</em>).</p> <p>The network is trained using synthetic data and evaluated through 5-fold cross-validation based on the Nash–Sutcliffe efficiency. The repository includes the workflow required to reproduce the DNN inference results presented in the associated study: Zanoni, M. G., de Barros, F. P.J., Bonazzi, A., and Bellin, A., <em>Enhancing Groundwater Contaminant Transport Forecasts Through Synergy of Machine Learning and Physics-Based Models</em>, submitted to Water Resources Research, 2026</p> |
| title | Deep neural network model for estimating transport parameters from breakthrough curves |
| url | https://doi.org/10.5281/zenodo.19551315 |