Chapter 2. Literature Review
2.9. Landslide studies in the Indian context
The SOP (GSI) states that an important aspect of landslide investigation called
“Stability Analysis” is seldom carried out. However, a landslide investigation cannot be completed without stability analysis, which is a direct measure of degree of instability of a slope. Proper geotechnical investigation of a landslide should be carried out to establish the causative factors of landslide. Detailed geotechnical mapping of landslide depicting geometry of the slide, crown and toe of the slide. Locations of longitudinal and transverse cracks, slide scars, old slide scar, plunge of striation lineation., Disposition of different litho-units, structure and state of weathering of rocks, type and composition of overburden. Location of severe toe erosion by streams or rivers, scouring pattern of slope along natural drainage, locations and attitude of steep, moderate and gentle slopes around the slide zone Zones of gully erosion, seepage/spring locations, dry or wet slope, elements at risk, tentative run out.
Such a study is essential for suggesting most appropriate remedial measures in a cost- effective manner to contain the slide.
Besides the above-mentioned codes and official documents, the IS 14680:1999 Landslide Control – provides some “Guidelines Landslide control methods for effective correction measures to avoid landslides in hill areas”.
The BIS codes for slope stability analysis, vis., IS 7894:1975 Code of Practice for Stability Analysis of Earth Dams and IS 8237:1985 Code of Practice for Protection of Slope for Reservoir Embankment are developed for, as the name suggest, for stability analysis of earth dams and reservoir embankment.
Saha et al. (2005) applied the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis for Landslide susceptibility zonation (LSZ) mapping in a part of the Himalayas. Slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and land cover are the factors considered in the study and have been generated using remote sensing and GIS techniques.
Pandey et al. (2007) carried out Landslide Hazard Zonation (LHZ) of Dikrong river basin of Arunachal Pradesh applying a weighting–rating system based on the relative importance of various causative factors, such as slope, photo-lineament buffer, thrust buffer, relative relief map, geology and land use / land cover map to calculate the Landslide Hazard Index (LHI) for each cell. The weight-rating values were re-adjusted using trial and error method.
Sarkar and Anbalagan (2008) used Landslide Hazard Evaluation Factor rating scheme for the landslide hazard zonation of an area of about 80 km2 to the south of the Alaknanda River in between Srinagar and Rudraprayag. The mapping was done in a macro level scale of 1:50,000.
Kuriakose et al. (2009) applied dynamic and distributed hydrological model (STARWARS) coupled with a probabilistic slope stability model (PROBSTAB) to the upper Tikovil River basin (55·6 km2). Figure 2.49(a) shows the general framework of the STARWARS and PROBSTAB models. STARWARS is a distributed dynamic hydrological model designed to evaluate the effects of vegetation on hillslope hydrology. Soil hydrological properties can be assigned to specific land use types. Unsaturated zone within the soil mantle over a semi-impervious basal rock contact can be modelled. PROBSTAB calculates the FoS based on the infinite slope model for the entire soil column, and if required the depth of failure, based on the daily variation of water level and volumetric moisture content, which are the outputs of STARWARS. Some modifications to the original physically based model were made to accommodate the data poor conditions in the region. Figure 2.49(b) the locations of shallow landslides plotted over predicted slope instability. The study showed that despite the poor input, the model captured the general temporal and spatial pattern of instability in the area.
Figure 2.49 (a) General framework of the STARWARS and PROBSTAB models; (b) Actual locations of shallow landslides plotted over predicted slope instability (Kuriakose et al., 2009)
Mathew et al. (2009) applied multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India.
The predictive logistic regression model has been validated by receiver operating characteristic curve analysis.
Jaiswal et al. (2010) used logistic regression model to determine the spatial probability of landslides within a transportation corridor of the Nilgiri Hills in southern India, taking the source area of the existing landslides as dependent, and slope angle, aspect, regolith thickness and land use as independent variables. The temporal probability of landslides was estimated indirectly using the exceedance probability of the rainfall threshold required to trigger landslides for the first time on natural slopes. The models were validated using the rainfall and landslide events that occurred during 2008 and 2009.
Sengupta et al. (2010) proposed an alternative rainfall threshold that predicts sliding if normalized cumulative rainfall more than a particular value within defined duration, for Lanta Khola debris slides characterized by fine-grained, low permeability debris material, highly susceptible to landsliding. Figure 2.50(a) shows a typical cross-section through the slide along with the strength of the slide materials and the results of the stability analyses.
Figure 2.50(b) shows the data of the local rainfall and landslide occurrence. The researchers suggested that rainfall threshold could not be defined by typical exponential relationships
between cumulative rainfall and rainfall duration based on the analysis of the available rainfall and landslide activity data for the area between 1998 and 2006.
Figure 2.50 (a) Slope stability analyses for the Lanta Khola Slide; (b) Rainfall and landslide data for the Lanta Khola slide, shown from January 1998 to the end of December 2006 (Sengupta et al., 2010)
Mani and Saranaathan (2017) conducted a landslide hazard zonation mapping on meso-scale (1:10,000) in SH-37 Ghat section, Nadugani, Gudalur, the Nilgiris, India applying the LHEF rating scheme (IS 14496, Part 2 1998) developed for macro level landslide hazard zonation in the scale of 1:50,000.
Ramesh et al. (2017) conducted macro landslide hazard zonation (LHZ) mapping and slope stability analyses of selected rock slope (RS) sections along 27 km Kuppanur–Yercaud Ghat road section, Tamil Nadu, India. 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.
Chawla et al. (2018) used Particle Swarm Optimization-Support Vector Machine technique and Genetic Programming method for the generation of landslide susceptibility map of an area of 201 km2. The study area is a part of the Darjeeling district, Eastern Himalaya. Drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect were considered as the influencing factors. Numerical weight and rating for each factor was assigned using the overlay analysis method. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low.
Dikshit et al. (2018) conducted in-situ study by installing tilt sensor and volumetric water content sensors for developing early warning and monitoring system with a low
probability of false alarms in Chibo Pashyor region in the state of West Bengal. The sensors were installed at shallow depths and was monitored for the tilting angle of the instrument, the variation of which corresponds to lateral displacement at slope surface. Figure 2.51(a) shows the surface tilt sensor that is equipped with a MEMS (micro-electro-mechanical systems) tilt sensor and a volumetric water content sensor. The time histories of the tilting angles of the sensors in X and Y directions are depicted in Figure 2.52. The volumetric water content and the recorded rainfall are plotted in Figure 2.51(b). The researchers concluded that antecedent rainfall of 3–10 days leads to displacement of slope that has also been observed earlier studies conducted in this region. With sufficient data from the system rainfall threshold can be validated as well as empirical equation based on site-specific conditions can be formulated.
Figure 2.51 (a) MEMS sensor and volumetric water content sensor (b) Detailed time history of volumetric moisture content for the in-situ sensors (Dikshit et al., 2018)
Figure 2.52 Time history of tilting angle in (a) parallel direction to slope, (b) tilting angle in perpendicular direction to slope (Dikshit et al., 2018)