Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard - Couverture rigide

Couverture rigide

ISBN 10 : 3036597867 ISBN 13 : 9783036597867

Editeur : Mdpi AG, 2023

Synopsis

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose a significant threat to people's lives and property. Recently, machine learning (ML) has become the predominant approach in geohazard modeling, offering advantages such as an excellent generalization ability and accurately describing complex and nonlinear behaviors. However, the utilization of advanced algorithms in deep learning remains poorly understood in this field. Additionally, there are fundamental challenges associated with ML modeling, including input variable selection, uncertainty quantification, and hyperparameter tuning. This reprint presents original research exploring new advances and challenges in the application of ML in the spatial-temporal modeling of geohazards. The contributions cover the susceptibility analysis of glacier debris flow and landslides, the displacement prediction of reservoir landslides, slope stability prediction and classification, building resilience evaluation, and the prediction of rainfall-induced landslide warning signals.

Détails de l'édition

• Éditeur: Mdpi AG
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 3036597867
• ISBN 13: 9783036597867
• Reliure: Relié
• Nombre de pages: 274
• Éditeur: Ma Junwei, Dou Jie
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible