Multi-sensor data fusion technology for the early landslide warning system

Chen, Musheng; Cai, Zhishan; Zeng, Yongxi; Yu, Yanzhong

doi:10.1007/s12652-022-04396-6

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Today, more research focuses on 3D geometric analysis to detect, monitor, and predict landslides (e.g., 3D change detection to compute volume or slope changes). In this section, we will describe the most common strategies to investigate landslides using 3D geometric analysis, including the conventional methods [3,18,43,80], fusion-based methods [24,[46][47][48][49], and artificial intelligence (AI)-based methods [17,65,80,96,97,[101][102][103][104][105][106]. Conventional methods are based on field surveys, manual calculation, visual interpretation of 3D data, and statistical and mathematical calculations of 3D data [3,18,43,80].…”

Section: D Geometric Analysis For Landslide Studiesmentioning

confidence: 99%

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Albanwan,

Qin,

Liu

2024

Remote Sensing

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Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.

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Section: D Geometric Analysis For Landslide Studiesmentioning

confidence: 99%

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Albanwan,

Qin,

Liu

2024

Remote Sensing

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“…Yuan et al built a BP neural network and comprehensively considered factors such as earthquakes, rainstorms, human activities, landslide displacement, slope and soil texture to predict and analyze the possible causes of landslides [36]. Musheng et al used a rainfall detector, global navigation satellite system and depth displacement sensor to monitor the internal states of landslides and related external factors, and based on generalized evidence theory, integrated monitoring data were used to make the final decision [37].…”

Section: Introductionmentioning

confidence: 99%

Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm

Lin

Sun

2023

Mathematics

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In landslide disaster warning, a variety of monitoring and warning methods are commonly adopted. However, most monitoring and warning methods cannot provide information in advance, and serious losses are often caused when landslides occur. To advance the warning time before a landslide, an innovative advance landslide prediction and warning model based on a stacking fusion algorithm using Baishuihe landslide data is proposed in this paper. The Baishuihe landslide area is characterized by unique soil and is in the Three Gorges region of China, with a subtropical monsoon climate. Based on Baishuihe historical data and real-time monitoring of the landslide state, four warning level thresholds and trigger conditions for each warning level are established. The model effectively integrates the results of multiple prediction and warning submodels to provide predictions and advance warnings through the fusion of two stacking learning layers. The possibility that a risk priority strategy can be used as a substitute for the stacking model is also discussed. Finally, an experimental simulation verifies that the proposed improved model can not only provide advance landslide warning but also effectively reduce the frequency of false warnings and mitigate the issues of traditional single models. The stacking model can effectively support disaster prevention and reduction and provide a scientific basis for land use management.

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Dempster–Shafer theory-based information fusion for natural disaster emergency management: A systematic literature review

Fei,

Li,

Ding

2024

Information Fusion

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Multi-sensor data fusion technology for the early landslide warning system

Cited by 4 publications

References 21 publications

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm

Dempster–Shafer theory-based information fusion for natural disaster emergency management: A systematic literature review

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