Assessing debris-flow hazard in a watershed in Taiwan

Lin, Ping‐Sien; Lin, Ji-Yuan; Hung, Jui-Chi; Yang, Ming‐Der

doi:10.1016/s0013-7952(02)00105-9

Cited by 118 publications

(73 citation statements)

References 5 publications

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“…The slope angle strongly influences the initiation and transportation of debris flows [7,36]. Previous research [5,37] shows that most debris flows occur in the areas with slope gradients higher than 15 • .…”

Section: Slope Anglementioning

confidence: 99%

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Wang

Chen

et al. 2017

Water

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Debris flows in the Wudongde dam area, China could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock engineering system and fuzzy C-means algorithm (RES_FCM) for debris flow susceptibility assessment. The watershed of the Jinsha River close to the Wudongde dam site in southwest China was taken as the study area, where a total of 22 channelized debris flow gullies were mapped by field investigations. Eight environmental parameters were selected for debris flow susceptibility assessment, namely, lithology, watershed area, slope angle, stream density, length of the main stream, curvature of the main stream, distance from fault and vegetation cover ratio. The interactions among these parameters and their weightings were determined using the RES method. A debris flow susceptibility map was produced by dividing the gullies into three categories of debris flow susceptibility based on the susceptibility index (SI) using the FCM algorithm. The results show that the susceptibility levels for nine of the debris flow gullies are high, nine are moderate and four are low, respectively. The RES based K-means algorithm (RES_KM) was used for comparison. The results suggest that the RES_FCM method and the RES_KM method provide very close evaluation results for most of the debris flow gullies, which also agree well with field investigations. The prediction accuracy of the new method is 90.9%, larger than that obtained by the RES_KM method (86.4%). Therefore, the RES_FCM method performs better than the RES_KM method for assessing the susceptibility of debris flows.

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Section: Slope Anglementioning

confidence: 99%

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Wang

Chen

et al. 2017

Water

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“…A review of methods for debris-flow hazard assessment, including those by Dai and Lee (2002), Lin et al (2002), Ayalew et al (2004), Lee and Pradhan (2007), Blahut et al (2010), Yune et al (2010), and Lee et al (2012), permits a comparative analysis of the influential attributes and hazard assessment methods of debris flows. Debris-flow hazards are dependent on a specific set of factors and processes that are usually investigated by various experts (such as hydrologists, geologists, or civil engineers).…”

Section: Literature Review Of Debris-flow Hazard Assessmentmentioning

confidence: 99%

A GIS-Based Framework for Real-Time Debris-Flow Hazard Assessment for Expressways in Korea

Kim

Chung

Kim

et al. 2016

Int J Disaster Risk Sci

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Debris flows caused by heavy rainfall in mountain areas near expressways lead to severe social and economic losses and sometimes result in casualties. Therefore, the development of a real-time system for debris-flow hazard assessment is necessary to provide preliminary information for rapid decision making about evacuations or restoration measures, as well as to prevent secondary disasters caused by debris flows. Recently, various map-based approaches have been proposed using multi-attribute criteria and assessment methods for debrisflow susceptibilities. For the macrozonation of debris-flow hazard at a national scale, a simplified method such as the Korea Expressway Corporation (KEC) debris-flow hazard assessment method can be applied for systematic analysis based on geographic information systems (GIS) and monitoring networks. In this study, a GIS-based framework of real-time debris-flow hazard assessment for expressway sections is proposed based on the KEC debris-flow hazard assessment method. First, the KEC-based method was standardized in a systematic fashion using ArcGIS, enabling the objective and quantitative acquisition of various attribute datasets. The quantification of rainfall criteria also was considered. A safety management system for debris-flow hazard was developed based on the GIS platform. Finally, the method was applied and verified on three expressway sections in Korea. The grading standard for each individual influencing attribute was subsequently modified to more accurately assess the debris-flow hazards.

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“…Traditional strategies are unlikely to provide a satisfactory solution, especially for small-area investigations where mass movement rates are slower [16,22]. For these reasons, researchers have adopted remote sensing techniques in their research methodology [8,9,20,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

et al. 2016

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Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ě λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

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Assessing debris-flow hazard in a watershed in Taiwan

Cited by 118 publications

References 5 publications

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

A GIS-Based Framework for Real-Time Debris-Flow Hazard Assessment for Expressways in Korea

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

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