Landslide susceptibility assessment using maximum entropy model with two different data sampling methods

Kornejady, Aiding; Ownegh, Majid; Bahremand, Abdolreza

doi:10.1016/j.catena.2017.01.010

Cited by 216 publications

(122 citation statements)

References 124 publications

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“…The slope determines how in uential that strength is in ensuring the movement of the object; however, the steeper the slope, the stronger the gravitational strength that ensures the shear of the slope and allows the object to slide. For this reason, slope steepness can be de ned as the risk factor tied to the most important soil factor that in uences slope stability (Kornejady et al, 2017). The slope angle directly affects the stress distribution in the slope.…”

Section: Effect Of the Slope Angle On Stabilitymentioning

confidence: 99%

Effect of the Slope Angle and Its Classification on Landslide

Çellek

2020

Preprint

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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.

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Section: Effect Of the Slope Angle On Stabilitymentioning

confidence: 99%

Effect of the Slope Angle and Its Classification on Landslide

Çellek

2020

Preprint

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“…The entropy method is an objective method for calculating the weight of evaluation predictors based on measured values [19,40,[64][65][66]. The operation steps are as follows:…”

Section: The Entropy Weight Methodsmentioning

confidence: 99%

“…The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze aerial photographs or to conduct field surveys [19].Data-driven landslide susceptibility assessment methods are used to select and analyze factors affecting landslides in areas with environmental conditions similar to those where past landslides have been reported [17]. They can be grouped in bivariate statistical analysis, multivariate statistical analysis and active learning statistical analysis.…”

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confidence: 99%

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The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Qin

Cao

et al. 2019

Entropy

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Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al. [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze...

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“…Once the information of the considered factors in the study was integrated, the Jackknife test was applied, which allows identifying the importance degree of each factor by executing the models in a separate way, with different sets of factors and excluding the factor analyzed. The Jackknife test provides alternative estimators of landslide patterns as a function of the factors, allowing so the identification of the significance of each of them, by their contribution degree and by their importance regarding the global results of the model [63][64][65][66].…”

Section: Evaluation Of the Influence Of Factors On The Occurrence Of mentioning

confidence: 99%

Characterization of Susceptible Landslide Zones by an Accumulated Index

Ramos-Bernal¹,

Vázquez-Jiménez²,

Tizapa³

et al. 2020

Landslides - Investigation and Monitoring

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In order to characterize the landslide susceptibility in the central zone of Guerrero State in Mexico, a spatial model has been designed and implemented, which automatically generates cartography. Conditioning factors as geomorphological, geological, and anthropic variables were considered, and as a detonating factor, the effect of the accumulated rain. The use of an inventory map of landslides that occurred in the past (IL) was also necessary, which was produced by an unsupervised detection method. Before the design of the model, an analysis of the contribution of each factor, related to the landslide inventory map, was performed by the Jackknife test. The designed model consists of a susceptibility index (SI) calculated pixel by pixel by the accumulation of the individual contribution of each factor, and the final index allows the susceptibility cartography to slide in the study area. The evaluation of the obtained map was performed by applying an analysis of the frequency ratio (FR) graphic, and an analysis of the receiver operating characteristic (ROC) curve was developed. Studies like this can help different safeguarding institutions, locating the areas where there is a greater vulnerability according to the considered factors, and integrating disaster attention management or prevention plans.

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Landslide susceptibility assessment using maximum entropy model with two different data sampling methods

Cited by 216 publications

References 124 publications

Effect of the Slope Angle and Its Classification on Landslide

Effect of the Slope Angle and Its Classification on Landslide

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Characterization of Susceptible Landslide Zones by an Accumulated Index

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