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The chain of circumstances that caused the Landslide data to fall into the hands of the British police will almost certainly never be repeated.
Based on the landslide data, landslides tended to occur on the medium and moderate area of CLSL.
The study area includes 390,837 pixels and the landslide data used in the model include 3827 pixels.
The level of accuracy of the landslide susceptibility map was verified by overlaying with the landslide data for validation.
Following the collection of the landslide data from various sources and their input into the database the consistency of the database was assessed.
Using the landslide data from Google Earth to GIS environment, we have to digitize the time series data from google earth image interpretation.
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The qualitative methods such as AHP subjectively help to rank the causal factors leading to classification of an area based on the priority scale whereas, the quantitative methods (bivariate or multivariate statistical analysis) use the observed landslide data for asserting the spatial relationship of the problem with the prevailing geo-environmental parameters (J. Corominas et al. 2014).
4) The results from the final map evaluations indicate that the 100% landslide data falls in the high susceptibility (HS) and very high susceptibility (VHS) classes and the spatial agreement between both the resultant maps as evaluated from error matrix method (Table 7) is more than 70%.
However, by comparing the CLSL model with the only landslide data inventory available in the research area, it will give an illustration of the accuracy of the resulted model.
Hence it can be concluded that, the landslide susceptibility map (LSM) generated from the AHP and frequency ratio method have yielded good results as the 100% landslide data falls in the high susceptibility and very high susceptibility classes of both the maps.
The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%.
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