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Ergebnis der Suche nach: "Machine Learning"
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Artikel 751 Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models
Enthalten in E-Journal of nondestructive testing Bd. 29, 2024, Nr. 7
Online Ressource
Artikel 752 Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data
Enthalten in E-Journal of nondestructive testing Bd. 29, 2024, Nr. 7
Online Ressource
Artikel 753 “Why Should I Trust You?”: Exploring Interpretability in Machine Learning Approaches for Indirect SHM
Enthalten in E-Journal of nondestructive testing Bd. 29, 2024, Nr. 7
Online Ressource
Artikel 754 A decomposition approach to evaluating the local performance of global streamflow reanalysis
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 15: 3597-3611. 15 S.
Online Ressource
Artikel 755 140-year daily ensemble streamflow reconstructions over 661 catchments in France
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 14: 3457-3474. 18 S.
Online Ressource
Artikel 756 Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 14: 3367-3390. 24 S.
Online Ressource
Artikel 757 Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 14: 3305-3326. 22 S.
Online Ressource
Artikel 758 To what extent do flood-inducing storm events change future flood hazards?
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 14: 3161-3190. 30 S.
Online Ressource
Artikel 759 When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 13: 3051-3077. 27 S.
Online Ressource
Artikel 760 Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models
Enthalten in Hydrology and earth system sciences Bd. 28, 2024, Nr. 11: 2531-2553. 23 S.
Online Ressource


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