GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China

Abstract

The main goal of this study is to produce landslide susceptibility maps for the Qianyang County of Baoji city, China, using both certainty factor (CF) and index of entropy (IOE) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 81 landslide locations were detected. Out of these, 56 (70%) landslides were randomly selected as training data for building landslide susceptibility models and the remaining 25 (30%) were used for the validation purposes. Then, a total number of 15 landslide causative factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, the sediment transport index (STI), the stream power index (SPI), the topographic wetness index (TWI), geomorphology, lithology, and rainfall, were used in the analysis. The susceptibility maps produced using CF and IOE models had five different susceptibility classes such as very low, low, moderate, high, and very high. Finally, the output maps were validated using the validation data (i.e., 30% landslide location data that was not used during the model construction), using the area under the curve (AUC) method. The 'success rate' curve showed that the area under the curve for CF and IOE models were 0.8433 (84.33%) and 0.8227 (82.27%) accuracy, respectively. Similarly, the validation result showed that the susceptibility map using CF model has the higher prediction accuracy of 82.32%, while for IOE model it was 80.88%. The results of this study showed that the two landslide susceptibility maps obtained were successful and can be used for preliminary land use planning and hazard mitigation purpose.

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

“…Numerous methods have been produced to assess landslide susceptibility at a regional scale, including direct geomorphological mapping (analysis of landslide inventories), heuristic approaches, statistical methods, physically-based models [7][8][9], and newly-developed machine learning models [10,11]. More detailed information of different models for landslide susceptibility mapping can be found in the literature [8,[12][13][14][15][16][17][18][19][20][21][22][23]. However, all the methods have both advantages and drawbacks, and no one method is accepted universally for the effective assessment of landslide hazards due to the complex nature of landslides [11].…”

“…Yao [9] trained one-class and two-class support vector machine (SVM) methods to map landslide susceptibility, comparing their accuracies with logistic regression, and concluded that two-class SVM possesses the best prediction result. Kavzoglu et al [18][19][20][21][22][23] evaluated landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and many other methods, and showed that SVM outperformed the conventional logistic regression method in the mapping of landslides. The above studies show the results in different regions with their own geo-environmental factors, and selecting a proper model should be performed for landslide susceptibility mapping through comparisons of landslide models.…”

Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan

“…The proximity of streams has been considered for the preparation of landslide susceptibility maps by many researchers (Akgun et al, 2008;Basharat et al, 2016;Kanwal et al, 2016;Wang et al, 2015). In our study area, many small tributaries feed main rivers (Indus and Hunza).…”

Landslide susceptibility mapping by using GIS along the China–Pakistan economic corridor (Karakoram Highway), Pakistan

“…The proximity of streams has been considered for the preparation of landslide susceptibility maps by many researchers (Akgun et al, 2008;Basharat et al, 2016;Kanwal et al, 2016;Wang et al, 2015). In our study area, many small tributaries feed main rivers, Indus and Hunza.…”