Distribution and genetic types of loess landslides in China

Zhuang, Jianqi; Wang, Shaokai; Wang, Qiyao; Zhuang, Jian; Wei-liang, Huang; Zhu, Xinghua; Leng, Yanqiu; Ma, Penghui

doi:10.1016/j.jseaes.2018.11.015

Cited by 152 publications

(37 citation statements)

References 35 publications

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“…Due to the large number and variability of landslide conditioning factors involved in the process, it is difficult to predict landslide-prone areas. What is known is that many geological and topographic conditions can influence the occurrence of landslides, such as formation lithology, faults, hydrology, attitude, slope angle, soil, and vegetation [8,9]. As landslide susceptibility mapping is the first and foremost step in landslide prevention, numerous researchers have been devoted to landslide susceptibility mapping in past years [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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“…A sudden acceleration was identified, which might be caused by the thawing activities in the loess, as shown in Figure 8b,c. Previous studies showed that the freeze and thawing activities are extremely harmful to the stability of loess slopes [4,46]. In general, the strength of frozen loess is much higher than that of natural loess.…”

Section: Possible Cause Of the Recent Xiangning Landslide Disastermentioning

confidence: 99%

“…Landslides are the most common geological disasters in the Loess Plateau in northwestern China. According to a recent study, 14,544 loess landslides are distributed in the Loess Plateau and the loess slope stabilities are affected by multiple factors such as soil properties, geomorphic structure, and rainfall [4]. In recent decades, changes in the natural environment together with intensive anthropogenic activities have caused frequent landslides.…”

Section: Introductionmentioning

confidence: 99%

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

et al. 2020

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A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.

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“…Loess is widely distributed throughout the arid and semi-arid regions of the world, accounting for approximately 10% of the global land area [1,2]. Loess has become a popular foundation of various engineering constructions because of its characteristics of simple development and low cost.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sodium Chloride Concentration on Saturated Permeability of Remolded Loess

Zhang

Qian

et al. 2020

Minerals

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Loess contains many sodium and chloride ions that can easily be leached when seepage occurs, thus affecting the mechanical properties of loess. This study investigated a series of sodium chloride solution concentrations to explore their influence on the permeability of remolded loess, as well as the underlying mechanism of such. The results indicated that the saturated hydraulic conductivity of remolded loess increases with time in response to different sodium chloride concentrations, and the sample was more permeable with increasing concentration. Moreover, the salt effect promoted the dissolution of calcite and dolomite, and the cation exchange stimulated the leaching of other cations, thus leading to further structural loosening. Furthermore, the aggregation of clay particles increased, thus forming a larger pore space among aggregates and providing effective channels for permeation. These findings provide a theoretical basis for an improved understanding of channel degradation in the loess area of Northwest China.

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Distribution and genetic types of loess landslides in China

Cited by 152 publications

References 35 publications

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

Effect of Sodium Chloride Concentration on Saturated Permeability of Remolded Loess

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