Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China

Lin, Wei; Yin, Kunlong; Wang, Ningtao; Yong, Xu; Guo, Zhengfu; Li, Yuanyao

doi:10.1007/s11069-020-04483-x

Cited by 31 publications

(19 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the landslide disaster [7], the soil and rock mass, which were originally a part of the mountain, leave the main body of the mountain due to gravity, and the cracks formed between the trailing edge of the landslide and the immobile mountain are mainly characterized as shown in Table 1.…”

Section: Characteristics Of Cracks At the Trailing Edge Of Landslidementioning

confidence: 99%

Texture Feature Extraction and Morphological Analysis of Landslide Based on Image Edge Detection

Wang

Wu²,

Hao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Landslides in nature are harmful to economic development and people’s lives and cause irreparable losses to the environment. With the application of image detection technology and intelligent algorithm, a new way for landslide detection is proposed to achieve effective detection and identification of hazards. This paper takes the landslide as the data set, carries on the noise reduction, the image expansion, and the image segmentation processing to the image, and extracts the object region information. The quantitative description of the azimuth displacement and displacement change of the crack curve is completed in this paper. This method is suitable for 3D simulation model, sand and stone model, soil model, and the sliding test results of Panzhihua Flight Field, which proves that the design method is effective. Experiments show that when sliding occurs, the texture and color become chaotic, the usual mountain becomes more in the regular state, and the extraction of features is very different. The method has better recognition effect for the hillside covered with vegetation, the recognition time is short, and the recognition rate can reach 90%.

show abstract

Section: Characteristics Of Cracks At the Trailing Edge Of Landslidementioning

confidence: 99%

Texture Feature Extraction and Morphological Analysis of Landslide Based on Image Edge Detection

Wang

Wu²,

Hao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Deterministic models based on landslide physics processes are widely used to predict the spatiotemporal stability of shallow landslides [41]. To evaluate the spatial distribution of the stability of shallow landslides caused mainly by rainfall, Borga et al [42] constructed a quantitative evaluation model of shallow landslide stability (Stability Index MaPping, SINMAP) based on the following: a DEM, a coupled steady-state hydrological model, and an infinite slope stability model [13,[20][21][22][23][24][25]. SINMAP is a large-scale slope stability model that uses topographic humidity index data obtained from the steady-state hydrological model, slope information obtained from the gridded DEM, maps of the effective catchment area and other GIS maps, and field investigation data.…”

Section: E Slope Stability Calculation With Sinmap Model Under the Combined Impact Of Rainfall And Vegetation Rootsmentioning

confidence: 99%

“…e main physical models used to evaluate landslides associated with rainfall are Shallow Slope Stability model (SHALSTAB) [11,12], the Stability Index MAPping model (SINMAP) [13,14], and the Transient Rainfall Infiltration and Grid-based Regional Slope Stability model (TRIGRS) [15][16][17][18][19]. e most recognized and widely used models are currently the SINMAP [13,[20][21][22][23][24][25]. e theoretical basis of using the SINMAP model arises from the fact that it is a large-scale slope stability model utilizing the terrain moisture index obtained from the steady-state hydrological model based on grid units that can be quickly and conveniently calculated.…”

Section: Introductionmentioning

confidence: 99%

A Regional Landslide Stability Analysis Method under the Combined Impact of Rainfall and Vegetation Roots in South China

Gong

Wang

Zhou

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

The aim of this study was to develop a regional landslide stability analysis method considering the combined impact of rainfall and the roots of vegetation in densely vegetated areas. A typical mountainous watershed in the Nanling National Nature Reserve of South China was chosen as the study area. First, the unmanned aerial vehicle (UAV) method was used to obtain surface element information including topography, vegetation, and landslides. Five main plant species were identified. The RipRoot model was then used to calculate the additional cohesion of these five plant species, and the relationship between the root systems of the different plant species and the soil shear strength was subsequently revealed. Finally, the root cohesion was introduced into the stability index mapping model (SINMAP), and the receiver operating characteristic curve (ROC) method was used to calculate the accuracy of slope stability when considering only soil cohesion as well as the composite cohesion of both soil and roots. The results showed significant differences in the root cohesion of different plants in the study area and a significant increase in the calculation accuracy (from 90% to 95.6%) when root cohesion was considered in the landslide stability calculation. These study results not only enrich theoretical studies on the impact of vegetation roots on landslide stability but also provide a scientific support for preventing disasters in mountainous landslide-prone areas.

show abstract

“…Generally, there are three main types of landslide hazard risk assessment methods: physical models, statistical models and machine learning methods [16][17][18][19]. A physical model is a mechanism model established according to the process of landslide occurrence [20,21]. Scholars at home and abroad have proposed many physical mechanism models, among which the SINMAP model is widely used [22,23].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

et al. 2022

View full text Add to dashboard Cite

Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau.

show abstract

Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China

Cited by 31 publications

References 47 publications

Texture Feature Extraction and Morphological Analysis of Landslide Based on Image Edge Detection

Texture Feature Extraction and Morphological Analysis of Landslide Based on Image Edge Detection

A Regional Landslide Stability Analysis Method under the Combined Impact of Rainfall and Vegetation Roots in South China

Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

Contact Info

Product

Resources

About