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This approach can contribute a new potential method to research scholars improving landslide evaluation and the quality of susceptibility mapping prediction.
However, the method could determine the performance of the output information, and the approach has improved the method of landslide evaluation for susceptibility mapping utilizing the LiDAR high-resolution DEM.
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The factor maps were input into a GIS and a modified landslide hazard evaluation factor (MLHEF) rating and fuzzy membership functions as well as belief function values were assessed for each class of the factor maps.
The macro LHZ map was prepared on 1 50,000 scale using landslide hazard evaluation factor (LHEF) rating scheme proposed by Bureau of Indian Standard IS 14496 (Part-2) 1998.
The BIS guidelines [11] were originally proposed by Anbalagan [3], which suggest a quantitative method based on conventional field surveys called landslide hazard evaluation factor (LHEF) rating scheme for Himalaya region.
The proposed experimental study is aimed at contributing to the landslide susceptibility evaluation using a multidisciplinary approach: geological, geomorphological and geo-engineering survey, together with multivariate statistical analysis and GIS technique.
The landslide susceptibility evaluation requires knowledge of factors leading to landslide analyses.
Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation.
This study discusses how we improve the quality of landslide susceptibility evaluation.
The probability of concurrent earthquake and rainfall event in this area requires landslide hazard evaluation by considering the effect of the unfortunate combination of rainfalls and earthquakes.
Recently, landslide hazard evaluation carried out by using fuzzy logic, and artificial neural network models (Lee et al. 2004; Yilmaz 2010; Lee et al. 2014).
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