Assessing Landslide Susceptibility Mapping (LSM) contribute in reducing the risk of living with landslides. Handling the vagueness associated with LSM is a challenging task. Here we show the application of hybrid GIS-based LSM. The hybrid approach embraces fuzzy membership functions (FMFs) in combination with Shannon entropy, a well-known information theory based method. Nine landslide-related criteria along with an inventory of landslides containing 106 recent and historic landslide points are used to prepare a susceptibility map. A random split into a train (≈ 70%) and test (≈ 30%) samples are used for training and validation of the LSM model. The study area – Izeh- is located in the Khuzestan province of Iran a highly susceptible Landslide zone. The performance of the hybrid method is evaluated using receiver operating characteristics (ROC) curves in combination with area under the curve (AUC). The performance of the proposed hybrid method with AUC of 0.963 is superior to multi-criteria evaluation approach using a subjective scheme, which was built on the basis of decision makers’ evaluation in the same study area.
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Roodposhti et al. (2016) studied this question.