A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties

Liu, Jia; Wu, Yuming; Gao, Xing; Zhang, Xuehua

doi:10.3390/rs14030668

Cited by 14 publications

(8 citation statements)

References 67 publications

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“…The multiple flow direction [40], Holmgren [41], modified Holmgren [42], cellular automata [43], and random walk [44,45] models provide opportunities to simulate possible runout paths. A combination of the empirical-statistical simplified friction-limited model (SFLM), which considers the reach angle, and flow direction algorithms, enables us to observe details such as feasible runout distances and paths using Flow-R software [42], as shown in recent studies [39,[46][47][48][49][50][51][52][53][54]. Owing to the development of computer technology, recent studies on landslide runout distance prepared with software go well beyond the conventional techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Flow-R software, which was developed at the University of Lausanne [42], was used to empirically examine shallow landslide runout scenarios in this study. This software offers researchers the opportunity to try a variety of landslide runout simulations [39,[46][47][48][49][50][51][52][53][54][60][61][62][63] to obtain runout distances by considering different parameter configurations. Table 1 provides a summary of previous Flow-R studies.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Komu,

Nefeslioglu,

Gokceoglu

2024

IJGI

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Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Komu,

Nefeslioglu,

Gokceoglu

2024

IJGI

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“…Time series classification (TSC) is regarded as one of the main challenges in data mining over the last several years [23]. Also, time series data have found many applications in image classification [24], healthcare [25], human recognition [26], and the steel industry [27]. This is because any classification problem using data that is arranged according to some notion of order can be taken as a TSC problem [20].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds

Han

Sánchez‐Azofeifa

2022

Remote Sensing

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The accurate separation between leaf and woody components from terrestrial laser scanning (TLS) data is vital for the estimation of leaf area index (LAI) and wood area index (WAI). Here, we present the application of deep learning time series separation of leaves and wood from TLS point clouds collected from broad-leaved trees. First, we use a multiple radius nearest neighbor approach to obtain a time series of the geometric features. Second, we compare the performance of Fully Convolutional Neural Network (FCN), Long Short-Term Memory Fully Convolutional Neural Network (LSTM-FCN), and Residual Network (ResNet) on leaf and wood classification. We also compare the effect of univariable (UTS) and multivariable (MTS) time series on classification accuracy. Finally, we explore the utilization of a class activation map (CAM) to reduce the black-box effect of deep learning. The average overall accuracy of the MTS method across the training data is 0.96, which is higher than the UTS methods (0.67 to 0.88). Meanwhile, ResNet spent much more time than FCN and LSTM-FCN in model development. When testing our method on an independent dataset, the MTS models based on FCN, LSTM-FCN, and ResNet all demonstrate similar performance. Our method indicates that the CAM can explain the black-box effect of deep learning and suggests that deep learning algorithms coupled with geometric feature time series can accurately separate leaf and woody components from point clouds. This provides a good starting point for future research into estimation of forest structure parameters.

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“…For the visual perception algorithms, they can be divided into traditional methods [9] and deep learning methods. Due to the complexity of smoke features, deep learning methods used for smoke detection often require many parameters and use a large amount of relevant data for training.…”

Section: Introductionmentioning

confidence: 99%

An Embedded Portable Lightweight Platform for Real-Time Early Smoke Detection

Liu

Sun

Cheng

et al. 2022

Sensors

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The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and higher hardware configuration, which disqualify them as lightweight portable smoke detection for high detection efficiency. To solve this challenge, this paper designs a lightweight portable remote smoke front-end perception platform based on the Raspberry Pi under Linux operating system. The platform has four modules including a source video input module, a target detection module, a display module, and an alarm module. The training images from the public data sets will be used to train a cascade classifier characterized by Local Binary Pattern (LBP) using the Adaboost algorithm in OpenCV. Then the classifier will be used to detect the smoke target in the following video stream and the detected results will be dynamically displayed in the display module in real-time. If smoke is detected, warning messages will be sent to users by the alarm module in the platform for real-time monitoring and warning on the scene. Case studies showed that the developed system platform has strong robustness under the test datasets with high detection accuracy. As the designed platform is portable without the involvement of a personal computer and can efficiently detect smoke in real-time, it provides a potential affordable lightweight smoke detection option for forest fire monitoring in practice.

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A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties

Cited by 14 publications

References 67 publications

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds

An Embedded Portable Lightweight Platform for Real-Time Early Smoke Detection

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