Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

ISBN 10: 3036597867 / ISBN 13: 9783036597867

Published by MDPI AG, 2023

About this Item

Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard | Buch | Englisch | 2023 | MDPI AG | EAN 9783036597867 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

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Bibliographic Details

• Title: Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard
• Publisher: MDPI AG
• Publication Date: 2023
• Language: English
• ISBN 10: 3036597867
• ISBN 13: 9783036597867
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 22D millimeters 250 millimeters width by 175 millimeters height by 22 millimeters depth
• Weight: 885 grams
• Seller catalogs:
  • Bücher

About this title

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.