The role of the lithological setting on the landslide pattern and distribution

Henriques, Cristina da Silva; Zêzere, José Luı́s; Marques, Fernando

doi:10.1016/j.enggeo.2015.01.025

Cited by 68 publications

(15 citation statements)

References 17 publications

(27 reference statements)

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“…The active faults are related to the recent Rift formation (late Cenozoic), while the inactive faults predate the WEAR (Paleozoic to early Cenozoic). Several studies have concluded that lithology has a strong impact on the spatial variation of landslide prevalence, type, and depth (Henriques et al, 2015). Based on the work of Laghmouch et al (2018) and field knowledge, we produced a new regional lithostratigraphical map for the WEAR, being the product for the region with the highest resolution ( Fig.…”

Section: Predictorsmentioning

confidence: 99%

The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift

et al. 2020

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Section: Predictorsmentioning

confidence: 99%

The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift

et al. 2020

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“…The variation of lithology may lead to the variation of strength and permeability of rock stratum. Usually, the landslide slides along a rock stratum with low strength and poor permeability [91][92][93][94]. The study area has ten lithology types ( Figure 2l).…”

Section: Landslide Conditioning Factorsmentioning

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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“…The lithology can be regarded as one of the most important factors in the geo-hazard assessment [49,59], because strength, the rocks, soil permeability, and weathering are influence by lithological characteristics [60,61]. The characteristics of the component materials of a slope such as permeability, porosity, and strength, play a crucial role in the stability of the slope, and are mostly different for various lithological units, so, this factor greatly influences the probability of landslide occurrence [58].…”

Section: Lithologymentioning

confidence: 99%

Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

et al. 2018

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The occurrence of landslide in the hilly region of South Korea is a matter of serious concern. This study tries to produce landslide susceptibility maps for Jumunjin Country in South Korea. Three machine learning algorithms, namely Logistic Regression (LR), LogitBoost (LB), and NaïveBayes (NB) are used, and their final model outcomes are compared to each other. Firstly, a landslide inventory map and the associated input data layers of the landslide conditioning factors were developed based on field verification, historical records, and high-resolution remote-sensing data in the geographic information system (GIS) environment. Seventeen landslide conditioning factors were prepared, including aspect, slope, altitude, maximum curvature, profile curvature, topographic wetness index (TWI), topographic positioning index (TPI), distance from fault, convexity, forest type, forest diameter, forest density, land use/land cover, lithology, soil, flow accumulation, and mid slope position. The result showed that the area under the curve (AUC) values of LR, LB, and NB models were 84.2%, 70.7%, and 85.2%, respectively. The results revealed that the LR and LB models produced reasonable accuracy than respect to NB model in landslide susceptibility assessment. The final susceptibility maps would be useful for preliminary land-use planning and hazard mitigation purpose.

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The role of the lithological setting on the landslide pattern and distribution

Cited by 68 publications

References 17 publications

The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift

The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift

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

Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

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