Introduction

Landslides occur due to various triggering factors such as heavy rainfall, earthquakes, changes in land cover etc.  These hydro-geomorphic events depend on the combination of  i) predisposing factors (e.g. lithology and  morphology), ii) triggering factors (e.g. excessive and intense precipitations) and iii) accelerating (e.g. human activities altering natural slope stability) factors³⁶ along with gravitational forces. Most common one is shallow landslide, occurring in steep, soil mantled landscapes in different climatic zones.^4,26,28,53 This event has a significant role in an ability to change surface erosion patterns on a hill slope and sediment yields to nearby streams, triggered due to changes in topography, land cover and soil properties. The occurrence time and locations where landslides are likely to occur should thus be identified and modeled in advance in order to avoid or reduce the casualties. Casualties due to landslides caused by slope failures are significantly higher in the undulating terrains, resulting in huge economic losses due to higher value of endangered structures and the greater number of property and human casualties.
             
Landslides in mountainous terrain often occur during or after heavy rainfall, resulting in the loss of life and properties. Mapping or delineating areas susceptible to landslides is essential for moderating land use activities and implementing appropriate mitigation measures with management options in mountainous areas.¹⁶ Prediction of rainfall triggered hill slope disasters relied mostly on the valley slope^8,20, rainfall intensity and duration that can cause hill slope failure.^7,9 During the last decade, theoretical models have been developed to predict landslide susceptibility based on topographic, geologic and hydrological variables as well as changes in watershed’s land use using various parameters such as neural, fuzzy, object oriented, knowledge based etc.^{15,24,30,37,38,40,49,50,63}

The availability of relatively detailed digital elevation data, coupled with simple slope-instability understanding and hill slope hydrological models, has led to refinements in physically-based modeling of shallow landslide hazards.^22,62 Daia and Leeb¹⁶ illustrated slope instability modeling using Geographical Information Systems (GIS). Casadei et al¹² predict the time and location of landslides through a landslide warning system using a slope-instability analysis and  hydrological models, which was validated by using historical data of landslide events for the period 1953–1998²⁹.

The failure depth prediction model¹³ is based on geotechnical properties (cohesion, angle of internal friction and density) of the soil and the local slope topography. Combination of failures and the consequent transportation of the failed materials lead to the evolution of a new hill-slope. These changes are due to the slope gradient, depending on the location of landslides, quantity and their run-out distances. There are numerous studies pertaining to shallow landslides across the globe^41,58,59 and  on landslide hazard zonation and modeling^3,24,51,52.

Most of the hazard appraisal techniques make use of Geographical Information Systems (GIS) along with temporal Remote Sensing data (RS) considering the complex spatial dynamics of the landslide process. Digital elevation models (DEMs) coupled with precipitation data at higher spatial and temporal resolution³⁶ aid in the shallow landslide hazard modeling.^{5,6,38,48,49,54,62} Methods based on the infinite slope stability parameters³¹ along with hydrological models^37,49,62, have been used for the estimation of soil wetness spatially.^2,11,18,37

Modelling these dynamic processes with appropriate methods require incorporation of all mechanisms acting at different spatial and temporal scale levels. However there are difficulties in the context of models of longer term landscape such as work on rapid mass movements has concentrated on stability analysis, so that forecasting of destinations for slide debris is very inexact, even for an individual slide²⁸, which has been addressed through various deterministic, heuristic and statistical based models to assess landslide incidence potential^1,10,17,43. Deterministic methods deal with the estimation of quantitative values of stability variables namely soil strength, depth below the terrain surface, soil layer thickness, slope angle and water pressure, etc. of the landslide region with homogeneous intrinsic properties^57,60 and are applicable at large scale over small areas. However, the drawback of these deterministic model techniques is high degree of simplification due to paucity of data and at times it is prohibitive to acquire the required data.