Debris-Flow Hazard Assessment and Methods Applied in Engineering Practice

Rickenmann, Dieter

doi:10.13101/ijece.9.80

Cited by 25 publications

(14 citation statements)

References 79 publications

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“…From, the thorough review of relevant literatures e.g., [38,60,115,149,150,151,152,153,154,155] twelve conditioning factors were selected and they were derived from the 2 m LiDAR DEM. These factors include; elevation (Figure 3a), plane curvature (Figure 3b), slope angle (Figure 3c), total curvature (Figure 3d), slope aspect (Figure 3e), Sediment Transport Index (STI) (Figure 3f), topographic profile curvature (Figure 3g), Topographic Roughness Index (TRI) (Figure 3h), flow accumulation/SCA (Figure 3i), Stream Power Index (SPI) (Figure 3j), Topographic Wetness Index (TWI) (Figure 3k), and Topographic Position Index (TPI) (Figure 3l).…”

Section: Methodsmentioning

confidence: 99%

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Lay

Pradhan

Yusoff

et al. 2019

Sensors

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Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

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Section: Methodsmentioning

confidence: 99%

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Lay

Pradhan

Yusoff

et al. 2019

Sensors

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“…1. triggering conditions (Ellen and Flaming, 1987;Gregoretti, 2000;Beylich and Sandberg, 2005;Wieczorek and Glade, 2005;Cannon et al, 2008;Tiranti et al, 2008;Stoffel et al, 2011Stoffel et al, , 2014Kean et al, 2013;Brunetti et al, 2015;Marra et al, 2015;Cavalli et al, 2017a) 2. propagation and deposition (Chang and Chao, 2006;Rickenmann et al, 2006;Deangeli et al, 2015;Gregoretti et al, 2016); 3. magnitude evaluation (Bovis and Dagg, 1988;Marchi and D'Agostino, 2004;Jakob et al, 2005;Hungr et al, 2008;Brardinoni et al, 2012;Rickenmann, 2015;Tiranti et al, 2016a;Cavalli et al, 2017b); 4. rheological behavior (Pierson and Costa, 1987;Costa, 1988;Hungr, 1995Hungr, , 2002Ancey, 2007;Von Boetticher et al, 2016); 5. geomorphological and sedimentary processes (Moscariello et al, 2002;Wilford et al, 2004); 6. evolution mechanisms (Sassa, 1985;Segre and Deangeli, 1995;Prancevic et al, 2014); 7. hydrologic modeling (Johnson and Sitar, 1990;Harvey, 1994;Hürlimann et al, 2006;Gregoretti et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Study to Evaluate Debris Flow Hazard in Alpine Environment

et al. 2018

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Debris flows are among the most dangerous natural processes affecting the alpine environment due to their magnitude (volume of transported material) and the long runout. The presence of structures and infrastructures on alluvial fans can lead to severe problems in terms of interactions between debris flows and human activities. Risk mitigation in these areas requires identifying the magnitude, triggers, and propagation of debris flows. Here, we propose an integrated methodology to characterize these phenomena. The methodology consists of three complementary procedures. Firstly, we adopt a classification method based on the propensity of the catchment bedrocks to produce clayey-grained material. The classification allows us to identify the most likely rheology of the process. Secondly, we calculate a sediment connectivity index to estimate the topographic control on the possible coupling between the sediment source areas and the catchment channel network. This step allows for the assessment of the debris supply, which is most likely available for the channelized processes. Finally, with the data obtained in the previous steps, we modeled the propagation and depositional pattern of debris flows with a 3D code based on Cellular Automata. The results of the numerical runs allow us to identify the depositional patterns and the areas potentially involved in the flow processes. This integrated methodology is applied to a test-bed catchment located in Northwestern Alps. The results indicate that this approach can be regarded as a useful tool to estimate debris flow related potential hazard scenarios in an alpine environment in an expeditious way without possessing an exhaustive knowledge of the investigated catchment, including data on historical debris flow events.

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“…In mountainous areas, debris flows and rock/snow avalanches represent an important hazard for mankind and infrastructures [1][2][3]. In the last decades, these phenomena have attracted huge interest from the scientific community, due to the increasing need to correctly describe their initiation and propagation phases, in order to better assess the risk, demarcate the hazardous areas and develop risk mitigation measures.…”

Section: Introductionmentioning

confidence: 99%

“…|g|T ji = hg i − 1 ρ τ bi T ij = hu i u j + g 3 2 h 2 g ij (1) where h is depth, t is time, g is the metric tensor, u k is the k-th component of the velocity vector, g i is the gravity vector, x k is the k-th space coordinate, T ij is the stress tensor, ρ is the density and τ bi the bed shear stress, to be defined by a specific rheology. As the proposed coordinate system is not orthogonal, the cross terms appear in the Equations.…”

Section: Introductionmentioning

confidence: 99%

Application of the 2D Depth-Averaged Model, FLATModel, to Pumiceous Debris Flows in the Amalfi Coast

Papa

Sarno

Vitiello

et al. 2018

Water

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Few studies about modelling pumice debris flows are available in literature. An integrated approach based on field surveys and numerical modelling is here proposed. A pumiceous debris flow, which occurred in the Amalfi Coast (Italy), is reconstructed by the numerical code, FLATModel, consisting of a two-dimensional shallow-water model written in curvilinear coordinates. The morphological evolution of the gully and of the alluvial fan was monitored by terrestrial laser scanner and photo-modelling aerial surveys, providing, in a cost-effective way, data otherwise unavailable, for the implementation, calibration and validation of the model. The most suitable resistance law is identified to be the Voellmy model, which is found capable of correctly describing the friction-collisional resistance mechanisms of pumiceous debris flows. The initial conditions of the numerical simulations are assumed to be of dam-break type: i.e., they are given by the sudden release of masses of pumice, whose shape and depths are obtained by reconstruction of the pre-event slopes. The predicted depths and shape of deposits are compared with the measured ones, where a good agreement (average error smaller than 10 cm) is observed for several dam-break scenarios. The proposed cost-effective integrated approach can be straightforwardly employed for the description of other debris flows of the same kind and for better designing risk mitigation measures.2 of 22 meaning that the computational domain is large and, hence, the choice of the mesh size is crucial. From a practical viewpoint this typically involves vertical integration and averaging of the hydraulic variables, leading to models of the shallow-water type [23][24][25][26].Moreover, the evaluation of the energy losses during the flow has to be suitably addressed. Generally speaking, a debris flow is made up of a heterogeneous mixture of solids and liquids. In nature, the fluid is water or a slurry of water and fine sediments (clay and silt), and the solid phase is represented by natural grain soils, with a size ranging from silt to boulders. The physics governing the mixture behaviour at the micro-scale is fully defined by the classical equations of mechanics, taking into account interactions within the solid phase, solid-fluid interactions and the dynamics of the fluid phase. Yet, modelling one by one every single particle in problems at the field scale becomes highly computationally expensive, and, often, is still beyond the capacities of the existing computational resources. Therefore, a macro-scale rheological approach should be selected, moving from the particle-fluid mechanics to the continuum mechanics. The scientific problem of the debris flow rheology is a consequence of this continuum mechanics approach, e.g., [27,28].The code FLATModel, firstly introduced by [20] and subsequently reformulated in curvilinear coordinates in [29], implements a two-dimensional depth-averaged mathematical model of the shallow-water type, specifically designed to describe the propagation of debris fl...

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Debris-Flow Hazard Assessment and Methods Applied in Engineering Practice

Cited by 25 publications

References 79 publications

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

An Integrated Study to Evaluate Debris Flow Hazard in Alpine Environment

Application of the 2D Depth-Averaged Model, FLATModel, to Pumiceous Debris Flows in the Amalfi Coast

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