Ancient landslide in Wanzhou District analysis from 2015 to 2018 based on ALOS-2 data by QPS-InSAR

Huang, Changjun; Qingshan, Zhou; Zhou, Lv; Cao, Yuzhen

doi:10.1007/s11069-021-04898-0

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…In this paper, Wanzhou District [24,25] of Chongqing City is taken as the research target. As shown in Figure 1, the thumbnail located at the bottom of this image provides a revealing glimpse into the selection process for the study area, which was carefully chosen from the Three Gorges reservoir area in the middle section of the Yangtze River in China.…”

Section: Study Area and Datamentioning

confidence: 99%

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

Song

Yang

et al. 2023

IJGI

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Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samples tend to be much smaller than non-landslide samples, is often overlooked. This problem is often one of the important factors affecting the performance of landslide susceptibility models. In this paper, we take the Wanzhou district of Chongqing city as an example, where the total number of data sets is more than 580,000 and the ratio of positive to negative samples is 1:19. We oversample or undersample the unbalanced landslide samples to make them balanced, and then compare the performance of machine learning models with different sampling strategies. Three classic machine learning algorithms, logistic regression, random forest and LightGBM, are used for LSA modeling. The results show that the model trained directly using the unbalanced sample dataset performs the worst, showing an extremely low recall rate, indicating that its predictive ability for landslide samples is extremely low and cannot be applied in practice. Compared with the original dataset, the sample set optimized through certain methods has demonstrated improved predictive performance across various classifiers, manifested in the improvement of AUC value and recall rate. The best model was the random forest model using over-sampling (O_RF) (AUC = 0.932).

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Section: Study Area and Datamentioning

confidence: 99%

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

Song

Yang

et al. 2023

IJGI

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show abstract

“…The Triassic system is primarily composed of limestone, claystone, sandstone, siltstone, and coal [36]. Frequent disastrous and complex landslide events in Wanzhou County have been the subject of research and susceptibility assessments in recent years due to its proximity to the Three Gorges Dam along the Yangtze River [39][40][41][42][43]. Heavy rainfall has been attributed as a significant trigger to the frequent landslides that have occurred [36,44,45].…”

Section: Study Areamentioning

confidence: 99%

A Framework to Project Future Rainfall Scenarios: An Application to Shallow Landslide-Triggering Summer Rainfall in Wanzhou County China

Ferrer

Guo

Medina

et al. 2022

Water

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Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments.

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“…Ferretti and Prati (2000) proposed Permanent Scatter InSAR (PS-InSAR), which maximizes the coherence of images on time series to overcome these limitations. It is an excellent solution for spatio-temporal incoherence, and it completely expresses its accuracy and superiority in monitoring surface subsidence (Xue et al 2016;Huang et al 2021;Wang et al 2018;Ge et al 2021). With the advancement of InSAR technology research, several researchers have proposed Small Baseline Subset InSAR (SBAS-InSAR) (Berardino et al 2002;Lanari et al 2004) as a measurement technology.…”

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confidence: 99%

Susceptibility evaluation of highway landslide disaster s based on SBAS-InSAR: A case study of S211 Highway in Lanping County

Liu

et al. 2022

Preprint

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Evaluation of landslide susceptibility along highways is critical for risk management in engineering development, construction, and operation and maintenance. The research target is the S211 Highway in Lanping County, Nujiang Prefecture, Yunnan Province, with its buffer zone extending 10 km as the research area. Eight evaluation factors are selected for the study, including slope, slope aspect, vegetation coverage, distance from the water system, rock group, rainfall, distance from the fault, and elevation. The findings of the susceptibility evaluation were classified into five categories, and the susceptibility grades of landslide disasters in the study area were evaluated using the information value and logistic regression coupling model. The accuracy of the coupling model was evaluated by the ROC curve and AUC value. The deformation rate in the study area was estimated by processing 28 Sentinel-1A satellite images captured from January to December 2019 using the SBAS-InSAR technology and was used to optimize the landslide susceptibility grade. The results show that the extremely high and high-risk areas of the information value-logistic regression coupling model account for 28.33% of the total area of the study area, which constitutes nearly 83.82% of the historical landslide disaster sites, mainly occupying areas along highways with low vegetation coverage and within 2000 m from rivers. The AUC values in the accuracy verification reach 0.843, indicating that the evaluation model can accurately predict the landslide susceptibility. The vulnerability grade of landslide geological disaster in the entire evaluation unit is significantly increased by optimizing the result of the surface deformation obtained by SBAS-InSAR technology. A total of 79587 grid units were added to the area, resulting in an extremely high vulnerability grade. This technique may optimize the evaluation results of landslide hazard susceptibility and provide decision support for disaster prevention and maintenance along highways.

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Ancient landslide in Wanzhou District analysis from 2015 to 2018 based on ALOS-2 data by QPS-InSAR

Cited by 11 publications

References 39 publications

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

A Framework to Project Future Rainfall Scenarios: An Application to Shallow Landslide-Triggering Summer Rainfall in Wanzhou County China

Susceptibility evaluation of highway landslide disaster s based on SBAS-InSAR: A case study of S211 Highway in Lanping County

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