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Understanding Landslide Susceptibility in The Great Xi'an Region, China: An Analytical Hierarchy Process Approach

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Ethan Sulliva
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Understanding Landslide Susceptibility in The Great Xi'an Region, China: An Analytical Hierarchy Process Approach

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Understanding the susceptibility of various regions to landslides is critical in planning and mitigating disaster risk. This is particularly true for areas with complex topographical features and diverse geological conditions, such as the Great Xi'an Region in China. This study aims to delineate landslide susceptibility maps for this region using the Analytical Hierarchy Process (AHP) method, a powerful tool in multi-criteria decision-making.

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Landslide Prone Analysis Using the Analytical Hierarchy Process

The AHP method was used to study multiple data sets, including elevation, slope, aspect, curvature, river density, soil lithology, and land use, to create spatially thematic layers and distributed maps in a GIS environment. By determining the relative importance of these thematic layers in the occurrence of landslides, sensitivity maps were generated and categorized into five levels: very high, high, moderate, low, and very low. When the LSM was overlaid with the test data, it was found that the moderate to very high landslide susceptibility zones could contain 82.58% of the historic landslides. These results contribute significantly to our understanding of landslide-prone areas in the region and will serve as a valuable reference for future construction and urban planning.

Comparative Landslide Susceptibility Mapping Approaches

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While the AHP method has proven useful in this context, other approaches to landslide susceptibility mapping have also been explored. For example, a novel landslide susceptibility prediction framework based on contrastive loss has been introduced in China's Zigui County. This framework utilizes the PU pullbaggingDT algorithm, which aims to address limitations in capturing correct negative samples in multi-genesis landslide areas. The algorithm has been shown to outperform existing PU learning and machine learning methods in predicting landslide susceptibility.

Applications of Landslide Susceptibility Mapping in Other Regions

Outside of China, landslide susceptibility mapping has been applied to various other regions. In Gujarat, India, for instance, a comprehensive framework for landslide risk assessment of archaeological sites integrates multi-criteria decision-making, satellite remote sensing, and Geographic Information Systems. This approach uses fifteen parameters to determine susceptibility and weights each one using the Analytical Hierarchy Process. Similarly, in Nepal, a fuzzy overlay approach based on freely available topographic data was used to determine landslide susceptibility. The results showed that landslide susceptibility is highest in the High Himalaya, while exposure is highest within the Middle Hills.

Implications for Urban Construction Planning

The results of this study are particularly crucial for urban construction planning in Shaanxi Province, China from 2021 to 2035. By identifying areas with high landslide susceptibility, the study provides invaluable data for informed decision-making in construction and land use planning. It also contributes significantly to the analysis of landslide susceptibility in the area and serves as a reference for other similar loess sites. As urbanization continues at a rapid pace, such studies will be increasingly important in minimizing the risk of landslide disasters and ensuring the safety and sustainability of our built environments.

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