| Téléchargement | - Voir la version finale : A machine learning approach for prediction of the quantity of mine waste rock drainage in areas with spring freshet (PDF, 2.5 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.3390/min13030376 |
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| Auteur | Rechercher : Zhang, CanIdentifiant ORCID : https://orcid.org/0009-0008-1014-424X; Rechercher : Ma, Liang1; Rechercher : Liu, Wenying |
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| Affiliation | - Conseil national de recherches du Canada. Énergie, les mines et l'environnement
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| Bailleur de fonds | Rechercher : National Research Council Canada; Rechercher : Natural Sciences and Engineering Research Council of Canada |
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| Format | Texte, Article |
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| Sujet | drainage flow model; spring freshet threshold; decision tree algorithm; LSTM algorithm |
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| Résumé | A new machine learning approach was developed to predict the quantity of mine waste rock drainage using weather data as the inputs. The novelty of the approach is that it includes spring freshet (melting of snow/ice in spring) as an input to the drainage flow rate model. Specifically, the machine learning approach integrates the decision tree algorithm to classify the occurrence or absence of spring freshet and a long short-term memory (LSTM) algorithm to predict the flow rate of mine waste rock drainage. The two algorithms are integrated by using the classification result of spring freshet as an input to the flow rate model. The machine learning approach developed was applied to predict the drainage flow rate at a case study mine in Canada. The model developed was trained with the local weather data as the inputs and the historical monitoring data of drainage flow rate as the target (output). The results show that the decision tree algorithm is able to classify the occurrence or absence of spring freshet with an accuracy of 91%. The inclusion of spring freshet as an input to the flow rate model significantly improves the performance of the flow rate model. The sensitivity tests show that changes in temperature and atmospheric precipitation influence the drainage flow rate. |
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| Date de publication | 2023-03-08 |
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| Maison d’édition | MDPI |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | f3971ef0-1412-4775-9c1a-e30ba3e59acd |
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| Enregistrement créé | 2024-03-08 |
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| Enregistrement modifié | 2024-03-08 |
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