A web-based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide

Li, Yuanyao; Huang, Jingsong; Jiang, Shui‐Hua; Huang, Faming; Chang, Zhilu

doi:10.1038/s41598-017-17507-7

Cited by 38 publications

(30 citation statements)

References 48 publications

(41 reference statements)

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“…The distance to river and TWI calculated from DEM and the MNDWI were used in this study as hydrological factors (Figure 3a-c). Distance to river reflects the water scour and soil erosion effects of surface runoff on landslide evolution [49], and TWI reflects the influence of terrain on soil saturation, rainfall infiltration, and runoff [50,51]. MNDWI suggests the surface humidity features, which indirectly present the moisture content in the slope mass [13].…”

Section: Landslide-related Predisposing Factorsmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

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Section: Landslide-related Predisposing Factorsmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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“…Different techniques and tools are now available for the measurement of ground displacement over time, from conventional wire extensometers (Corominas et al 2000), inclinometers (Zhang et al 2018), GPS (Li et al 2017) and levelling (Cotecchia et al 1995;Colesanti et al 2003) to more innovative approaches, such as UAV (Unmanned Aerial Vehicle) photogrammetry (Peternel et al 2017;Rossi et al 2018) and terrestrial laser scanning (Fanti et al 2013;Frodella et al 2016). These techniques rely on the materialisation of a network of geodetic benchmarks designed to cover the extent of the likely moving area.…”

Section: Introductionmentioning

confidence: 99%

Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy)

et al. 2019

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The results of the continuous monitoring of ground deformation throughout the Tuscany region using radar images acquired by the Sentinel-1 satellite constellation of the European Space Agency (ESA) are presented here. This new monitoring approach, based on systematic imagery processing and analysis of deformation time series, is discussed at regional (for the entire Tuscany) and at local scale in the context of a case study of the Carpineta landslide, which is a large, active earth slide in the Northern Apennines (Pistoia province). The landslide registered an acceleration during the winter and spring of 2018 as a direct consequence of rainfall and snow melt. The increase in the deformation rate of the landslide, which led to the damage of several buildings, was promptly detected and monitored due to the enhanced temporal repetitiveness offered by the Sentinel-1 constellation. The results demonstrate that advances in satellite sensors, increases in computing capacity and the refinement of processing approaches and data screening tools can contribute to the development of new paradigms in satellite-based monitoring systems. Sentinel-1 data, which are systematically acquired with short revisiting times and then promptly processed, can now be used as a tool for the systematic tracking of ground deformation at the regional scale and for the continuous monitoring of slow and very slow landslides.

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“…Along with the development of information science and electronic technologies, 3S technology has shown wide application prospects. The 3S can constitute a strong technical system to quickly and accurately acquire, analyse, update, save and display many kinds of spatial and attribute information (Li et al 2017). In this study, GPS is used to position the collapses and to measure the boundaries of collapses.…”

Section: '3s' Technology For Collapse Susceptibility Assessmentmentioning

confidence: 99%

“…In this study, GPS is used to position the collapses and to measure the boundaries of collapses. Then, the location and boundary information of the collapses are imported into the GIS software for data saving and spatial analysis (Li et al 2017). RS is mainly used to obtain the causal factors of collapses, such as land cover factors, topographic factors and hydrological factors, from different kinds of remote sensing images (i.e., Landsat 8 and global digital elevation model (GDEM)) (Katagis et al 2014;Dou et al 2015).…”

Section: '3s' Technology For Collapse Susceptibility Assessmentmentioning

confidence: 99%

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

Sheng

Chai

et al. 2020

Geomatics, Natural Hazards and Risk

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Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility assessment (CSA). This paper proposes a 3S (RS, GPS and GIS) technologybased support vector machine (SVM) to map collapse susceptibility in the Nantian area of China. The 3S technology-based backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) models are also proposed for comparison. First, 44 recorded collapses are identified through field investigation, and fourteen collapse-related causal factors are acquired using 3S technology. Second, among these recorded collapses and randomly selected 'non-collapses', 70% of the collapse and non-collapse grid cells are used to train the three models, while the remaining 30% are used to test the models. Third, the collapse susceptibility maps of the Nantian area are produced using the three models. Finally, the prediction accuracies of these models are evaluated. The results indicate that the SVM model has the highest prediction accuracy, while the RBFNN model has the lowest prediction accuracy for CSA. In addition, the distribution characteristics of collapse susceptibility in the Nantian area are produced well by all three models. ARTICLE HISTORY

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A web-based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide

Cited by 38 publications

References 48 publications

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy)

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

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