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Fig. 2 Landslide data inventories.
In this study landslide inventories were divided into landslide data for training and landslide data for validation.
Landslide data is used for the validation of landslide hazard zonation map.
Overlaid landslide data validation on LSM will describe another level of accuracy beside AUC curve.
Next, the ratio was obtained by overlaying landslide data for validation into the landslide susceptibility map.
Subsequently, the AUC of predictive rate was obtained by using landslide data for validation.
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An equal number of landslide and non-landslide data for training with landslide causal factors were combined as a merge variable in SPSS.
Synthetic hourly rainfall-landslide data generated by Monte Carlo simulations are analysed to compute return periods as the mean interarrival time of a factor of safety less than one.
This difference highlights the lack of large-landslide data, lack of chronology, problems associated with single catchment/large landslide-based calculations, and the need for regional landscape-lowering determinations over a standardized time period.
In this study, the logistic regression model is developed using equal proportion of landslide and non-landslide pixels in ten iterations and using 50%% and all non-landslide data as comparison.
Therefore, we collected the existing inventory spatial distribution of landslides data, Google Earth's images, LiDAR point clouds, and ASTER data to study landslides probability prediction of the Alborz Mountains in Iran.
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