Volume estimation of small scale debris flows based on observations of topographic changes using airborne LiDAR DEMs

Kim, Ho‐Sung; Lee, Seung Woo; Yune, Chan-Young; Kim, Gi Hong

doi:10.1007/s11629-013-2829-8

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…Furthermore, the quality of the results strongly depends on the surveyors' experience, skills and knowledge of the pre-event terrain (Scheidl et al 2008). Therefore, the use of various remote sensing techniques has been reported for debris flow mapping, including: High-resolution satellite imaging (Youssef et al 2016;Elkadiri et al 2014), manned-aircraft photography (Dietrich and Krautblatter 2016), airborne laser scanning (ALS) (Kim et al 2014;Bull et al 2010;Scheidl et al 2008) or a combination of the above (Willi et al 2015). However, compared with other natural hazard events (e.g., floods, earthquakes), debris flows affect relatively small areas (usually < 5 km 2 ).…”

Section: Uav For Debris Flow Mapping and Analysismentioning

confidence: 99%

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

Giordan

Adams

Aicardi

et al. 2020

Bull Eng Geol Environ

218

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This paper represents the result of the IAEG C35 Commission "Monitoring methods and approaches in engineering geology applications" workgroup aimed to describe a general overview of unmanned aerial vehicles (UAVs) and their potentiality in several engineering geology applications. The use of UAV has progressively increased in the last decade and nowadays started to be considered a standard research instrument for the acquisition of images and other information on demand over an area of interest. UAV represents a cheap and fast solution for the on-demand acquisition of detailed images of an area of interest and the creation of detailed 3D models and orthophoto. The use of these systems required a good background of data processing and a good drone pilot ability for the management of the flight mission in particular in a complex environment.

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Section: Uav For Debris Flow Mapping and Analysismentioning

confidence: 99%

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

Giordan

Adams

Aicardi

et al. 2020

Bull Eng Geol Environ

218

View full text Add to dashboard Cite

show abstract

“…Gregoretti et al [5] proposed a GIS-based model tested against field measurements for a rapid hazard mapping. Kim et al [6] used a high-resolution light detection and ranging (LiDAR) digital elevation model to calculate the volume of debris flows. Kim et al [7] developed a GIS-based real-time debris flow susceptibility assessment framework for highway sections.…”

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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“…Airborne LiDAR was used by [6,32,51] to estimate deposition volumes of debris flows. [31,44] used terrestrial laser scanning (TLS) to determinate the dynamics of a slow moving landslide and debris flows for subsequent model simulations.…”

Section: Discussionmentioning

confidence: 99%

Evaluation concepts to compare observed and simulated deposition areas of mass movements

2017

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The simulation of geophysical mass flows, including debris flows, rock and snow avalanches, has become an important tool in engineering hazard assessment. Especially the runout and deposition behaviour of observed and expected mass flows are of interest. When being confronted with the evaluation of model performance and sensitivity, there are no standard, objective approaches. In this contribution, we review methods that have been used in literature and outline a new approach to quantitatively compare 2D simulations of observed and simulated deposition pattern. Our proposed method is based on the comparison of normalized partial areas which can be plotted in a ternary diagram to visualize the degree of over-and under-estimation. Results can be summed up by a single metric between -1 (no fit) and 1 (perfect fit). This study shall help developers and end-users of simulation models to better understand model behaviour and provides a possibility for comparison of model results, independent of simulation platform and type of mass flow.

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Volume estimation of small scale debris flows based on observations of topographic changes using airborne LiDAR DEMs

Cited by 12 publications

References 37 publications

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

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

Evaluation concepts to compare observed and simulated deposition areas of mass movements

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