GPS Monitoring Landslide Deformation Signal Processing using Time-series Model

Huang, Faming; Wu, Panfeng; Ziggah, Yao Yevenyo

doi:10.14257/ijsip.2016.9.3.28

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…GPS has been proven enable millimetre-accurate recording of slope failure, but more random errors, introduced during signal transmission and by interferences, which still need to be carefully mitigated to achieve millimetre accuracy (Wang 2013). The process of it could be complicated sometimes in related to factors of ionospheric and tropospheric influences (Huang et al 2016). Conversely, while BIs only afford measurement magnitudes, being as much as a few centimetres per 30 m (Stark and Choi 2008), their associated random errors, as in situ instruments, are smaller.…”

Section: Possible Displacement Based On Gps and Bis Error Estimationmentioning

confidence: 99%

“…On the other hand, there are kinds of errors and inherited weaknesses in landslide observations, which may bring disadvantages to slope stability estimation. For example, random errors in BIs could be smaller than 1mm but the their measurement magnitude is as much as a few centimetres per 30 m deep (Stark and Choi 2008); on the contrary, GPS affords millimetre-accurate landslide movement observations (Wang 2013), but the rough results contain more random noises in related to ionospheric and tropospheric influences (Huang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Liu

Shao

2018

Geomatics, Natural Hazards and Risk

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Multi-sensor observation is very important for monitoring landslide disasters. Since various surveying techniques are currently available for detecting variational slope activities from different perspectives, studies have focused on integration of multi-source information for the analysing landslide displacements. In this study, a general multi-source data fusion scheme for landslide monitoring based on three-dimensional variation (3DVar) data assimilation was developed. The scheme was used to fuse different observations of Xishancun Landslide in Li County, Sichuan Province, China. First, the displacement observations obtained by a Global Positioning System (GPS) and Borehole Inclinometers (BIs) were assimilated for accurate evaluation of slope activities. Then, slope Stability Index (SI) was introduced to validate the assimilation results within a time interval. SI Assi values calculated using the integration model developed in the present study were compared with SI FS simulated by a physically based landslide model. The correlation coefficient between them ss 0.75, which is larger than those with SI GPS (0.45) or SI BIs (0.41) values determined by the GPS and BIs respectively. The assimilation results are thus confirmed to be more credible for slope stability simulation.

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Section: Possible Displacement Based On Gps and Bis Error Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Liu

Shao

2018

Geomatics, Natural Hazards and Risk

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“…These cracks threaten the lives and property of local residents. To monitor the landslide deformation, three groundwater stage gauges, two global positioning system (GPS) points [33,34] and one rain gauge were installed. Point GPS-1 was located on the upper part of the landslide, and point GPS-2 was located on the frontal part of the landslide.…”

Section: Landslide Deformation Characteristics and Stability Analysismentioning

confidence: 99%

Stability Analysis of Hydrodynamic Pressure Landslides with Different Permeability Coefficients Affected by Reservoir Water Level Fluctuations and Rainstorms

Huang

Luo

Liu

2017

Water

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Abstract:It is significant to study the variations in the stability coefficients of hydrodynamic pressure landslides with different permeability coefficients affected by reservoir water level fluctuations and rainstorms. The Sifangbei landslide in Three Gorges Reservoir area is used as case study. Its stability coefficients are simulated based on saturated-unsaturated seepage theory and finite element analysis. The operating conditions of stability coefficients calculation are reservoir water level variations between 175 m and 145 m, different rates of reservoir water level fluctuations, and a three-day continuous rainstorm. Results show that the stability coefficient of the hydrodynamic pressure landslide decreases with the drawdown of the reservoir water level, and a rapid drawdown rate leads to a small stability coefficient when the permeability coefficient ranges from 1.16 × 10 −6 m/s to 4.64 × 10 −5 m/s. Additionally, the landslide stability coefficient increases as the reservoir water level increases, and a rapid increase in the water level leads to a high stability coefficient when the permeability coefficient ranges from 1.16 × 10 −6 m/s to 4.64 × 10 −5 m/s. The landslide stability coefficient initially decreases and then increases as the reservoir water level declines when the permeability coefficient is greater than 4.64 × 10 −5 m/s. Moreover, for structures with the same landslide, the landslide stability coefficient is most sensitive to the change in the rate of reservoir water level drawdown when the permeability coefficient increases from 1.16 × 10 −6 m/s to 1.16 × 10 −4 m/s. Additionally, the rate of decrease in the stability coefficient increases as the permeability coefficient increases. Finally, the three-day rainstorm leads to a significant reduction in landslide stability, and the rate of decrease in the stability coefficient initially increases and then decreases as the permeability coefficient increases.

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“…It is essential to acquire collapse inventory and related causal factor information in the first step. In recent decades, 3S technology, which closely and organically combines remote sensing (RS), globe positioning system (GPS) and geographic information system (GIS) technologies, is widely used in the areas of environmental protection, natural disaster analysis and natural resources surveying (Pradhan 2010;Huang et al 2016a;Chang et al 2020). Hence, we introduce 3S technology to address the issues of acquiring data sources and providing a calculation platform for CSA.…”

Section: Introductionmentioning

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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GPS Monitoring Landslide Deformation Signal Processing using Time-series Model

Cited by 21 publications

References 19 publications

Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Stability Analysis of Hydrodynamic Pressure Landslides with Different Permeability Coefficients Affected by Reservoir Water Level Fluctuations and Rainstorms

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

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