ArticleScientific reports2024
Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi'an Region, China.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 61 citations in OpenAlex.
- Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment.Scientific reports · 2025Article
- Study on comprehensive landslide risk zoning method in open pit mines under multi factor coupling effects.Scientific reports · 2025Article
- Evaluation of coseismic landslide susceptibility by combining Newmark model and XGBoost algorithm.PloS one · 2025Article
- Innovative design and evaluation of medical nebulizer for preschool children: A user demand-driven approach.PloS one · 2025Article
- Research on the application of AHP-FAST-FBS in the design of home entrance disinfection devices in the post-pandemic era.Scientific reports · 2024Article
- Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to delineate landslide susceptibility maps using the Analytical Hierarchy Process (AHP) method for the Great Xi'an Region, China, which is a key planning project for urban construction in Shaanxi Province, China from 2021 to 2035. Multiple data as elevation, slope, aspect, curvature, river density, soil, lithology, and land use have been considered for delineating the landslide susceptibility maps. Spatially thematic layers and distributed maps of all the aforementioned parameters were created in a GIS environment. Determine the relative importance of these thematic layers in the occurrence of landslides in the study area concerning historical landslide data to assign appropriate weights. Landslide sensitivity maps were generated by a weighted combination in a GIS environment after being analyzed by the AHP method. The sensitivity maps were categorized as "very high (11.06%), high (19.41%), moderate (23.03%), low (28.70%), and very low (17.80%)". Overlay analysis of the test data with the LSM showed that the moderate to very high landslide susceptibility zones were able to contain 82.58% of the historic landslides. The results of the study help determine the landslide-prone areas in the area and provide a reference for subsequent construction. In addition, the analysis of landslide susceptibility in the area contributes to the study of landslides in similar loess sites.
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Registered trials
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