An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping
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An improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping using a landslide inventory of Chongqing, China, which contains 8435 landslides demonstrates that all three methods perform well in evaluating landslide susceptibility.Abstract:
Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.read more
Citations
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A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment
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Applying different scenarios for landslide spatial modeling using computational intelligence methods
TL;DR: In this article, the authors applied different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats.
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Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity
TL;DR: Results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area, demonstrating its promise in validating different landslide susceptibility maps.
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GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China
TL;DR: In this article, a comprehensive landslide susceptibility mapping was attempted with an integrated weighted index model by combining the frequency ratio and the analytical hierarchy process approaches under a GIS-based environment in the heavily earthquake-affected Zhangzha town of the Jiuzhaigou region.
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The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping
TL;DR: The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes.
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