Effect of the Slope Angle and Its Classification on Landslide

Slope Angle ◽

Advantages And Disadvantages ◽

Susceptibility Maps ◽

Complex Process ◽

Landslide Distribution ◽

Landslide Susceptibility Maps ◽

Abstract The preparation of landslide susceptibility maps is a complex process with regards to the selection of the study field, parameters, and methods. The phase after the determination of the landslide distribution in landslide susceptibility studies is the selection of the methods and parameters to be used. However, first, as in this study, a comprehensive literature search is required. A review of approximately 1500 randomly selected publications revealed that it was necessary to select a parameter based on the area, and the research showed that, in each study, the most preferred parameter was the slope angle. Moreover, there is nearly a consensus of opinion among researchers regarding the use of the slope angle. The current research included the definitions of the slope angle put forth by different researchers, the advantages and disadvantages of its use, the different classifications that have been used, the intervals of the landslides, its use together with other parameters, and its effect on landslides. Generally, it was observed that automatic slope angle classifications have been used in the preparation of landslide maps in the literature. Therefore, there is no standard in slope angle maps nor in the class range that is referenced when preparing them. In this study, the class ranges and slope angle values of areas where landslides have occurred were determined from the literature, and of these, 40 landslides areas were selected in Turkey and their slope angle maps were created. These were evaluated according to the slope angle classes determined in the literature. The effects of the slope angle on the landslide were determined, and an understanding was gained of how important it was to be careful when determining the classification of the slope angle. When smaller class ranges were selected, different results were obtained. This showed that following parameter selection, the selection of the range of classes was vital in the preparation of landslide susceptibility maps.

Effect of the Slope Angle and Its Classification on Landslide

10.5194/nhess-2020-87 ◽

2020 ◽

Author(s):

Seda Çellek

Keyword(s):

Slope Angle ◽

Landslide Area ◽

Advantages And Disadvantages ◽

Definition Of ◽

Landslide Maps ◽

Abstract. The phase after the determination of the landslide area in landslide susceptibility studies is the selection of methods and parameters to be used. Approximately 1500 randomly selected publications show that it is necessary to select a parameter based on the area. Research has shown that the parameter of slope is greatly preferred. There is nearly consensus of opinion among researchers regarding the use of the parameter. The research included the definition of slope made by different researchers, the advantages and disadvantages of the use of the parameter, different classifications that are used, the formation intervals of landslides, their use together with other parameters, and its effect on the formation of landslides. Classifications were studied based on the slope values at which landslides. Generally, automatic slope classifications are used in the preparation of landslide maps. There isn’t standard in parameter maps. Therefore, there isn’t class range that is referenced when preparing slope maps. In this study, preferred class ranges and slope values where landslides occur were determined in the literature. 40 landslides area has been selected in Turkey. These were evaluated in the slope classes determined according to the literature. The results compared with the literature were found to be compatible.

Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽

10.3390/sym12030325 ◽

2020 ◽

Vol 12 (3) ◽

pp. 325 ◽

Cited By ~ 20

Author(s):

Guirong Wang ◽

Xinxiang Lei ◽

Wei Chen ◽

Himan Shahabi ◽

Ataollah Shirzadi

Keyword(s):

Radial Basis Function Network ◽

Slope Angle ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Training Dataset ◽

Validation Dataset ◽

Slope Aspect ◽

Susceptibility Maps ◽

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120696 ◽

2020 ◽

Vol 9 (12) ◽

pp. 696

Author(s):

Wei Chen ◽

Zenghui Sun ◽

Xia Zhao ◽

Xinxiang Lei ◽

Ataollah Shirzadi ◽

...

Keyword(s):

Vegetation Index ◽

Slope Angle ◽

Spatial Prediction ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Bivariate Model ◽

Susceptibility Maps ◽

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Natural Hazards ◽

10.1007/s11069-021-04599-8 ◽

2021 ◽

Author(s):

Anna Małka

Keyword(s):

Logistic Regression ◽

Mass Movement ◽

Slope Angle ◽

Geographic Information ◽

Slope Aspect ◽

Susceptibility Map ◽

Conditioning Factors ◽

Susceptibility Maps ◽

AbstractThis work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

Proceedings of the 4th Regional Symposium on Landslides in the Adriatic - Balkan Region ◽

Significance of landslide susceptibility maps in creation of spatial planning documentation

10.35123/resylab_2019_22 ◽

2019 ◽

Author(s):

Kenan Mandžić ◽

Nedreta Kikanović ◽

Adnan Ibrahimović

Keyword(s):

Spatial Planning ◽

Susceptibility Maps ◽

DEVELOPMENT OF LANDSLIDE SUSCEPTIBILITY MAPS IN COLORADO

10.1130/abs/2016am-287630 ◽

2016 ◽

Author(s):

Kassandra Lindsey ◽

◽

Matthew L. Morgan ◽

Karen A. Berry

Keyword(s):

Susceptibility Maps ◽

Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps

Geomorphology ◽

10.1016/j.geomorph.2016.03.015 ◽

2016 ◽

Vol 262 ◽

pp. 8-23 ◽

Cited By ~ 45

Author(s):

Stefan Steger ◽

Alexander Brenning ◽

Rainer Bell ◽

Helene Petschko ◽

Thomas Glade

Keyword(s):

Susceptibility Maps ◽

Quantitative Validation ◽

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽

10.3390/e20110884 ◽

2018 ◽

Vol 20 (11) ◽

pp. 884 ◽

Cited By ~ 28

Author(s):

Tingyu Zhang ◽

Ling Han ◽

Wei Chen ◽

Himan Shahabi

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Shaanxi Province ◽

Slope Aspect ◽

Support Vector ◽

Operating Characteristics ◽

Study Region ◽

Susceptibility Maps ◽

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

Regression analysis and statistical examination of Knoop Hardness on abrasion resistance in Lyca Beige Marbles

Filomat ◽

10.2298/fil2002609a ◽

2020 ◽

Vol 34 (2) ◽

pp. 609-614

Author(s):

Burcu Aydin ◽

Fusun Yalcin ◽

Ozge Ozer ◽

Gurhan Yalcin

Keyword(s):

Mechanical Properties ◽

Regression Analysis ◽

Physical And Mechanical Properties ◽

Production Method ◽

Knoop Hardness ◽

Decomposition Products ◽

Marble Quarries ◽

Marbles are secondary decomposition products formed by metamorphism of limestone. Effective classification of marble quarries in terms of quality enables the selection of a sustainable production method and safety application. This evaluation is based on physico-mechanical properties of the samples. Obtained results of physico-mechanical properties of the marbles were statistically analyzed using Stata 14 and SPSS 21 software. The marbles indicated mostly normal physical and mechanical properties. A strong inverse relationship exists between Abrasion Value and Knoop Hardness Determination that indicates a significant nonlinear relationship. Samples were distinguished into 3 groups of close similarity and related properties. The estimated value of the parameters is in the 95 % confidence interval. The equation obtained by regression analysis was used for the determination of resistance to abrasion.