1Department of Geology, Cotton University, Guwahati-781001, India.
2Department of Geological Sciences, Gauhati University, Guwahati-781014, India
*Corresponding Author: Santanu Sarma, Department of Geology, Cotton University, Panbazar, Guwahati, Kamrup (M), Assam-781001, India. E-mail: santanugw1@yahoo.com
In recent time some detrimental effects of climate change has been observed in NE India: landslides and scale of floods have been increasing annually, misty mornings during Christmas no longer occurs, wet-bulb temperatures (TW) during summers have increased, and so on. However, the landslide susceptibility literature begins with the premise that future hillslope failures are likely to occur under the conditions of past and present instability. Moreover, most of the land surface parameters considered for the landslide studies is not possible to make field identification (except slope, aspect, plan and profile curvature). However, in the study area field the following observations are made-
Earth slides occur only at sites where regolith thickness is between a range, neither too thick nor too thin.
The dip angle of the gneissic bedrock surface has a more precise negative correlation with the regolith thickness than the slope of the ground surface.
Almost all profile curvatures are convex upward, but despite these, earth slides have occurred.
There are a few convergent plan curvatures, but earth slides are not restricted only to these sites.
The reasons why these observations are contradictory to popular belief are under preparation as a full-length article, but in short, the conclusion is that statistical learning combined with field observation for applying some obvious mechanistic knowledge of the process is the key to successful landslide susceptibility assessment.
Landslide Susceptibility, Statistical Learning, Field-Observation, Bedrock, Regolith
