Abstract
The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.
Highlights
Landslide susceptibility denotes the probability of a landslide occurring in an area based on the local geo-environment [1,2,3]
Different models have been used for landslide susceptibility mapping, such as an analytical hierarchy process [7,8,9], logistic regression [10,11], an artificial neural network [12,13], support vector machines [14,15], the entropy method, and the frequency ratio method [16]
The rock group was a combination of soft- and medium-hardness substrates, which were prone to moderate-to-large landslides
Summary
Landslide susceptibility denotes the probability of a landslide occurring in an area based on the local geo-environment [1,2,3]. Landslide occurrence is related to various factors, such as precipitation, geology, distance-to-fault, vegetation, and topography, which altogether encompass the attributes of landslide susceptibility mapping [4,5,6]. Different models have been used for landslide susceptibility mapping, such as an analytical hierarchy process [7,8,9], logistic regression [10,11], an artificial neural network [12,13], support vector machines [14,15], the entropy method, and the frequency ratio method [16]. Many methods use either a subjective evaluation or an objective evaluation, which limited itself the efficacy of the approach. It is necessary to combine the subjective and objective evaluation methods [17]
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