A siphon drainage system with variable diameters for landslides: Concept, calculation, and validation

Zheng, Jinhai; Wang, Jiongchao; Guo, Jichao; Zhang, Yingqiu; Lü, Qing; Sun, Hongyue

doi:10.1016/j.jhydrol.2021.126305

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…Landslides could have disastrous consequences for the life and economy in mountainous regions worldwide (Lü et al, 2021; Sun et al, 2021; Yu et al, 2021; Zheng et al, 2021). Slope surface displacement is one of the most intuitive demonstrations of the development and stability of the landslide (Cheng et al, 2021; Ge, Liu, et al, 2021; Wei et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A data‐driven intelligent model for landslide displacement prediction

Sun

Liu

et al. 2023

Geological Journal

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Landslides with step‐like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi‐input and multi‐output intelligent integrated displacement prediction model for landslides with step‐like displacement patterns. In this new model, three interconnected and information‐transmitted functional sub‐models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management.

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