Case study of debris flow disaster scenario caused by torrential rain on Kiyomizu-dera, Kyoto, Japan - using Hyper KANAKO system

Nakatani, Kana; Hayami, Satoshi; Satofuka, Yoshifumi; Mizuyama, Takahisa

doi:10.1007/s11629-015-3517-7

Cited by 17 publications

(14 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To treat debris flow, we selected a two-dimensional simulation model based on the dilatant fluid model developed by Takahashi (2007), which has been frequently applied in practice (Wu et al, 2013;Nakatani et al, 2016). The governing equations can be written as ,…”

Section: Simulation Methodology 21 Governing Equationsmentioning

confidence: 99%

“…As the initiation process includes many unknowns resulting from a lack of in-situ observations, a number of assumptions are made in conducting debris-flow simulations. The first is to assume the hydrograph and sediment concentration values at the inlet of the target domain Chen et al, 2017;Han et al, 2018;Bao et al, 2019;Nakatani et al, 2016;Frank et al, 2015;Gao et al, 2016). In this method, the inlet point is usually set as the initiation point or as the location at which the debris flow is sufficiently developed.…”

Section: Treatment Of Debris Flow Initiationmentioning

confidence: 99%

“…Given this limitation, model simulations are typically carried out using a hydrograph that flows from the outlet of the debris-flow-prone catchment (e.g., Rickenmann et al (2006); Nakatani et al (2016)). In general, such hydrographs are modeled using rainfall-runoff simulation or rainfall data processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive Simulation of Concurrent Debris Flow: How Slope Failure Locations Affect Predicted Damage

Yamanoi¹,

Oishi²,

Kawaike³

et al. 2020

Preprint

View full text Add to dashboard Cite

Predictive simulation of concurrent debris flow using only pre-disaster information has proven to be difficult as a result of problems in predicting the location of debris-flow initiation (i.e., slope failure). However, because catchment topography has concave characteristics, with all channels in a catchment joining each other as they flow downstream, it is possible to predict damage to downstream area using relatively inaccurate initiation points. Based on this, this paper presents methodologies employing debris-flow initiation points generated randomly using statistical slope failure prediction. A many-case simulation across numerous initiation points was performed to quantify the effect of slope-failure location in terms of deviations in the predicted water level and terrain deformation. It was found that the relative standard deviation diminished as the points approached the downstream area, indicating a location-based predictability effect. (131 words) Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 8 April 2020

show abstract

Section: Simulation Methodology 21 Governing Equationsmentioning

confidence: 99%

Section: Treatment Of Debris Flow Initiationmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Simulation of Concurrent Debris Flow: How Slope Failure Locations Affect Predicted Damage

Yamanoi¹,

Oishi²,

Kawaike³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Consequently, resulted in the loss of life and property. The event usually triggered by a number of factors such as heavy precipitation, lahar, earthquake, landslide and other anthropogenic activities [4,5,6,7,8,9,10]. Noteworthy, the prominent factors trigger debris-flow events in Malaysia is torrential rainfall that transforms landslides to a ribbon of surge liquid and solid sediments forming the debris flows [11].…”

Section: Introductionmentioning

confidence: 99%

“…The studies rely on surface-based rainfall observations near debris source region and proven effective in identifying rainfall intensity threshold for mobilizing debris in regions prone to mass-wasting [29]. Nakatani et al [10] investigated the debris flow disaster scenario caused by severe precipitation in a stream of Kiyomizu (Kyoto, Japan). Nakatani and his co-researchers developed a Hyper KANAKO 2D system based on a numerical model to simulate debris flows using rainfall intensity, archived landform data, DEM and digital surface (DSM).…”

Section: Introductionmentioning

confidence: 99%

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

Lay

Pradhan

Yusoff

et al. 2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

Runout analysis of a potential debris flow in the Dongwopu gully based on a well-balanced numerical model over complex topography

Han

Chen

et al. 2017

Bull Eng Geol Environ

View full text Add to dashboard Cite

Case study of debris flow disaster scenario caused by torrential rain on Kiyomizu-dera, Kyoto, Japan - using Hyper KANAKO system

Cited by 17 publications

References 3 publications

Predictive Simulation of Concurrent Debris Flow: How Slope Failure Locations Affect Predicted Damage

Predictive Simulation of Concurrent Debris Flow: How Slope Failure Locations Affect Predicted Damage

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

Runout analysis of a potential debris flow in the Dongwopu gully based on a well-balanced numerical model over complex topography

Contact Info

Product

Resources

About