Development of an initiation criterion for debris flows based on local topographic properties and applicability assessment at a regional scale

Kang, Sinhang; Lee, Seung-Rae; Vasu, Nikhil Nedumpallile; Park, Joonyoung; Lee, Deukhwan

doi:10.1016/j.enggeo.2017.09.017

Cited by 34 publications

(13 citation statements)

References 32 publications

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“…Among machine learning methods, BPNN is widely used because it carries the excellent nonlinear fitting and complex learning abilities to extract the complex relationship between debris flow triggering factors and DFS [24]. Convolutional neural network, a classical deep learning method, has been rapidly developed in the past decade and is widely used in pattern recognition and medicine It is generally used for classification and recognition of two-dimensional images.…”

Section: Introductionmentioning

confidence: 99%

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

et al. 2019

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Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

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Section: Introductionmentioning

confidence: 99%

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

et al. 2019

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“…We used a digital elevation model (DEM) with a 10 m-cell size computed from 1:5000 digital maps produced by the National Geographic Information Institute of Korea [37] (see Figure 1). Approximately 160 catastrophic shallow landslides occurred in this area owing to heavy rain on 26-27 July 2011, with a cumulative total of 470 mm over the two days [39]. Debris flows that resulted from slope failures occurred along 20 watersheds from 7:00 to 9:00 local time on 27 July 2011, resulting in 16 deaths and severe structural damage to 10 buildings.…”

Section: Landslide Inventories and Spatial Datamentioning

confidence: 99%

Effects of Two-Phase Flow of Water and Air on Shallow Slope Failures Induced by Rainfall: Insights from Slope Stability Assessment at a Regional Scale

Kang

Cho

Kim

et al. 2020

Water

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Over 160 shallow landslides resulted from heavy rainfall that occurred in 26–27 July 2011 at Umyeon Mountain, Seoul, South Korea. To accurately reflect the fluid flow mechanism in the void spaces of soils, we considered the two-phase flow of water and air for rainfall infiltration analysis using available historical rainfall data, topographic maps, and geotechnical/hydrological properties. Variations in pore water and air pressure from the infiltration analysis are used for slope stability assessment. By comparing the results from numerical models applying single- and two-phase flow models, we observed that air flow changes the rate of increase in pore water pressure, influencing the safety factor on slopes with a low infiltration capacity, where ponding is more likely to occur during heavy rainfall. Finally, several slope failure assessments were conducted to evaluate the usefulness of using the two-phase flow model in forecasting slope stability in conditions of increased rainfall sums. We observed that the two-phase flow model reduces the tendency of over-prediction compared to the single-phase model. The results from the two-phase flow model revealed good agreement with actual landslide events.

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“…where S w is the saturation of the wetting fluid, and S g is the saturation of the non-wetting fluid. The difference between the pressures of the two fluids is the capillary pressure, as expressed by Equation (2). Equation (3), proposed by van Genuchten [14], illustrates a unique relationship between the capillary pressure and the effective saturation related to both the actual saturation and the residual saturation.…”

Section: Unsaturated Flowmentioning

confidence: 99%

“…The slope failures triggered by rainfall have frequently been reported worldwide [1]. In Korea, mountainous areas occupy approximately 70 percent of the territory, and 70 percent of the annual rainfall higher than 1000 mm is concentrated during a rainy season that occurs between June and September [2]. Numerous slope failures occur almost every year during the rainy season due to localized heavy rains, resulting in loss of life and property damage [3].…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Analysis of Unsaturated Soil Slopes Based on the Site-Specific Characteristics: A Case Study of Hwangryeong Mountain, Busan, Korea

2020

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Shallow slope failures occur almost every year during the rainy season. Continuous observation of the meteorological parameters and hydrological characteristics is required to more clearly understand the triggering mechanisms of shallow slope failure. In addition, influential factors, such as type of relative permeability models, air flow, and variation of hydraulic conductivity associated with stress–strain behavior of soil, have significant effects on the actual mechanism of rainfall infiltration. Real-time data including hourly rainfall and pore water pressure in response to rainfall was recorded by devices; then, the change in pore pressure from the devices was compared to the results from the infiltration analysis with applications of three relative permeability models, air flow, and the coupled hydro-mechanical analysis to examine an appropriate site-specific approach to a rainfall infiltration analysis. The infiltration and stability analyses based on the site-specific hydrologic characteristics were utilized to create maps of safety factors that depend on the cumulative rainfall. In regions vulnerable to landslides, rainfall forecast information and safety factor maps built by applying various rainfall scenarios can be useful in preparing countermeasures against disasters during the rainy season.

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Development of an initiation criterion for debris flows based on local topographic properties and applicability assessment at a regional scale

Cited by 34 publications

References 32 publications

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

Effects of Two-Phase Flow of Water and Air on Shallow Slope Failures Induced by Rainfall: Insights from Slope Stability Assessment at a Regional Scale

Slope Stability Analysis of Unsaturated Soil Slopes Based on the Site-Specific Characteristics: A Case Study of Hwangryeong Mountain, Busan, Korea

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