Rainstorm-induced large-scale landslides in Northeastern Chongqing, China, August 31 to September 2, 2014

Li, Bin; Gao, Yang; Yin, Yueping; Wan, Jiawei; He, Kai; Wu, Weile; Zhang, Han

doi:10.1007/s10064-022-02763-3

Cited by 19 publications

(5 citation statements)

References 27 publications

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“…It is also one of the areas with a high incidence of landslide hazards. It is therefore very important to study the postfailure and risk assessment of landslide disasters in this area and to improve the technology of disaster prevention and mitigation in this area (Gao et al, 2022;Li et al, 2022).…”

Section: Xianchi Reservoir Landslidementioning

confidence: 99%

Numerical Simulation of Rainfall‐induced Xianchi Reservoir Landslide in Yunyang, Chongqing, China

YAN,

MA,

LIU

et al. 2024

Acta Geologica Sinica (Eng)

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A calamitous landslide happened at 22:00 on September 1, 2014, in the Yunyang area of Chongqing City, southwest China, forcing 508 people death and 23 buildings damage. The landslide volume includes 1.44 million m3 of material in the source area and 0.4 million m3 of shoveled material. The debris flow's runout extended 400 meters vertically and 1600 meters horizontally. The researched results are listed as follow: (1) the Xianchi reservoir landslide is investigated in detail; (2) the samples collected from the main body of landslide are carried out by GCTS ring shear apparatus, and the parameters of shear and pore water pressure are measured; (3) the post‐failure characteristics of landslide are analyzed by using the numerical simulation method. It is considered that the excess pore water pressure and erosion in the motion path are the key reasons for the long‐runout motion and scale‐up of landslides caused by the heavy rainfall. The aim of this paper is to acquired numerical parameters and the basic resistance model, which is beneficial to improve simulation accuracy for hazard assessment similar to potential hillslopes in Southwest China.

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Section: Xianchi Reservoir Landslidementioning

confidence: 99%

Numerical Simulation of Rainfall‐induced Xianchi Reservoir Landslide in Yunyang, Chongqing, China

YAN,

MA,

LIU

et al. 2024

Acta Geologica Sinica (Eng)

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“…Fengjie County is a landslide disaster-prone area, and it has experienced several large-scale landslides that have significantly impacted people's daily lives. For example, a large landslide occurred on Wushanping in September 2014 under the influence of rainfall, which damaged more than 1200 houses and caused significant economic losses (Li et al, 2022); landslides similar to that of the Xinpu landslide, with a height difference of 634.4 m, a slope length of approximately 2 km, and a total volume of 394 350.3 m 3 , can occur at any time (Zhu et al, 2021). Therefore, the LSM in Fengjie County can provide a scientific reference for local disaster risk management and urban construction planning.…”

Section: Study Areamentioning

confidence: 99%

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

Sun,

Ding,

Wen

et al. 2023

Earth Surf Processes Landf

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Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research.

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“…1e). These account for 72 % of the total failures over the 9 examined years, and were triggered by an extreme storm (Li et al, 2022). The vast majority of landslides manifest as shallow translational slides.…”

Section: Landslide Inventorymentioning

confidence: 99%

From space-time landslide susceptibility to landslide risk forecast

Wang¹,

Dahal²,

Fang³

et al. 2023

Preprint

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The literature on landslide susceptibility is rich with examples that span a large number of topics. However, the component that pertains to the extension of the susceptibility framework toward space-time modeling is largely unexplored. This statement is even more valid when looking at the landslide risk context, where hardly any scientific contribution investigates risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). To perform the analyses, the data cube is organized with a mapping unit made of slope units (26,333) repeated over an annual temporal unit (for a total of 236,997). This phase already features a number of interesting modeling experiments that have hardly appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, for we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These are also combined with the projection of urban settlements and associated populations to obtain a dynamic risk model (under the assumption that vulnerability = 1). Overall, this is a unique example of such a modeling routine and a potential standard to be followed for administrations to make informed decisions on future urban development.

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Rainstorm-induced large-scale landslides in Northeastern Chongqing, China, August 31 to September 2, 2014

Cited by 19 publications

References 27 publications

Numerical Simulation of Rainfall‐induced Xianchi Reservoir Landslide in Yunyang, Chongqing, China

Numerical Simulation of Rainfall‐induced Xianchi Reservoir Landslide in Yunyang, Chongqing, China

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

From space-time landslide susceptibility to landslide risk forecast

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