Dynamic process of a typical slope debris flow: a case study of the wujia gully, Zengda, Sichuan Province, China

Yao, Shunyu; Bazai, Nazir Ahmed; Tang, Jingqun; Hu, Juyang; Yi, Shujian; Qiang, Zhimin; Ahmed, Tashfain; Guo, Junbo

doi:10.1007/s11069-021-05194-7

Cited by 7 publications

(3 citation statements)

References 85 publications

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“…In addition, the development of UAV technology in recent years has provided a simple tool for acquiring high spatial resolution data at the centimeter level. Highprecision and high-resolution Digital Elevation Model (DEM) images acquired by UAVs are widely used in soil erosion research [26,27] and disaster risk assessment research [28]. Moreover, a large number of studies leveraged UAV multispectral data to identify land use types [29] and vegetation types [30,31].…”

Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

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Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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“…Andrade et al [5] measured and studied the physical properties of debris flow using seismic apparatus. Yao et al [6] analyzed the impacts of landform on the movement process of debris flow. Pal et al [7] developed a sensitive risk model for debris flow movement.…”

Section: Introductionmentioning

confidence: 99%

A study of control scheme of debris flows and geological disasters in the Shiwei river basin

2024

Archives of Civil Engineering

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The basic characteristics of debris flows in the Shiwei river basin are summarized through the field investigation on debris flows in the Shiwei river basin and analysis on formation conditions of debris flows from three aspects, i.e. geological environment, geological structure and neotectonic movement, as well as seismic action. Based on this, the stability of landslide in the Shiwei river basin is analyzed and calculated, and the stability coefficient of landslide is obtained. The debris flows in the Shiwei river basin will directly damage and threaten the county town, while other geological disasters such as landslide, collapse, slope sliding & collapse and potentially unstable slopes will indirectly damage and threaten the county town. The landslide form is clear, and the landslide stability calculation shows that the landslide body is generally stable – basically stable, but partially unstable – less stable. The “blocking + discharging” comprehensive control scheme is proposed according to the formation conditions and development characteristics of debris flows in the Shiwei river basin, and the study findings can be used as a reference for similar projects.

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“…In the field of hazard simulation, researchers commonly utilize the Water 2023, 15, 3140. https://doi.org/10.3390/w15173140 https://www.mdpi.com/journal/water shallow water equations (SWEs) as the governing equations. These equations are applied to studying a range of hazards, including flash floods [3][4][5], debris flows [6,7], landslides [8], and dam breaches [9][10][11]. Solving the SWEs is essential to capture the dynamics of these hazards, necessitating the development of an effective and efficient solver by model developers [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations

Yao,

Kan,

Liu

et al. 2023

Water

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In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges in discretizing convection flux terms, while theory-based models need to account for various factors such as shock wave capturing and wave propagation direction, demanding a high-level understanding of the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading to weak generalization properties when terrain data or stress constitutive relations change. To overcome these limitations, this study introduces a novel SWE solver that combines theory and data-driven methodologies. The core idea is to use artificial neural networks to compute convection flux terms, and to reduce modeling complexity. Theory-based modeling is used to tackle complex terrain and friction terms for the purpose of ensuring generalization. Our method surpasses challenges faced by previous DL-based solvers in capturing terrain and stress variations. We validated our solver’s capabilities by comparing simulation results with analytical solutions, real-world disaster cases, and the widely used Massflow software-generated simulations. This comprehensive comparison confirms our solver’s ability to accurately simulate hazard scenarios and showcases strong generalization on varying terrain and land surface friction. Our proposed method effectively addresses DL-based solver limitations while simplifying the complexities of theory-driven numerical methods, offering a promising approach for hazard dynamics simulation.

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Dynamic process of a typical slope debris flow: a case study of the wujia gully, Zengda, Sichuan Province, China

Cited by 7 publications

References 85 publications

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

A study of control scheme of debris flows and geological disasters in the Shiwei river basin

A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations

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