| Téléchargement | - Sera disponible ici le 9 août 2026
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| DOI | Trouver le DOI : https://doi.org/10.1016/B978-0-443-13293-3.00019-1 |
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| Auteur | Rechercher : Cai, Xiatong; Rechercher : Mohammadian, Abdolmajid; Rechercher : Cobo, Hiedra Juan1; Rechercher : Shirkhani, Hamidreza1Identifiant ORCID : https://orcid.org/0000-0002-0893-652X; Rechercher : Imanian, Hanifeh; Rechercher : Payeur, Pierre |
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| Affiliation | - Conseil national de recherches Canada. Construction
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| Format | Texte, Chapitre de livre |
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| Résumé | The prediction of soil temperature under climate change plays an important role in understanding hydrological processes. Genetic programming can create a mathematical equation that can be used for predictions with very high efficiency due to its explicit analytical form. However, it is rarely used in soil temperature prediction, especially in extremely hot weather conditions. The fitness of multigene genetic programming (MGGP) in ordinary weather was found to be R² = 0.97, and R² = 0.83 in extremely hot weather. We compared the performance of single-gene genetic programming (SGGP) and multigene genetic programming (MGGP) with benchmark linear and AI models. Results show that the MGGP algorithm outperforms linear models and is comparable with some distance-based and tree-based benchmark AI models in both ordinary and extremely hot weather. MGGP underperformed the artificial neural network. Using only a polynomial equation rather than executing a complicated model with a large input dataset, MGGP shows good simplification in soil temperature prediction. |
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| Date de publication | 2024-08-09 |
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| Maison d’édition | Elsevier |
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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 | d779c85d-9968-49e8-bfea-a4cf1257a836 |
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| Enregistrement créé | 2023-11-02 |
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| Enregistrement modifié | 2024-08-23 |
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