期刊
REMOTE SENSING
卷 13, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/rs13183623
关键词
fuzzy analytical hierarchy process; landslide susceptibility; landslide types; Loess Plateau; logistic regression; weighted frequency ratio
类别
资金
- Fundamental Research Funds for the Central Universities [lzujbky-2021-kb12]
- China Postdoctoral Science Foundation [2021M691370]
- National Natural Science Foundation of China [52109125]
- National Postdoctoral Program for Innovative Talent of China [BX20200191]
This study focused on landslide susceptibility zonation in Qilihe District, Lanzhou City, incorporating landslide classification and various influencing factors through a data-driven model. The results showed that the majority of landslide pixels were classified as high or very high susceptibility zones, with a satisfactory model accuracy. Consideration of landslide typology significantly improved the accuracy of the susceptibility assessment maps.
Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into very high or high susceptibility zones, while very low or low susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5 similar to 5.4%.
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