Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

Ma, Junwei
Dou, Jie

Hardcover

General Science

ISBN10: 3036597867
ISBN13: 9783036597867
Publisher: Mdpi AG
Published: Dec 29 2023
Pages: 274
Weight: 1.74
Height: 0.88 Width: 6.69 Depth: 9.61
Language: English

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.

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