4.2 Relationship between Landslide Causative Factors and
Landslide Occurrence
LSM maps of the study area were prepared using three bivariate
statistical models and were further analysed for landslide
susceptibility change due to road construction. This was done by
preparing two types of LSM maps. The first map, termed as Landslide
Susceptibility Map Natural (LSMN), was formulated,
taking into account only ten landslide causative factors excluding
distance from road factor. Similarly, the second map, termed as
Landslide Susceptibility Map Road (LSMR), was prepared,
taking into account all the eleven landslide causative factors,
including distance from the road factor, using Equation (4). The
LSMN and LSMR maps were classified into
five landslide susceptibility zones: very low, low, moderate, high, and
very high susceptibility, as shown in Figure 5.
The FR values of each class of eleven landslide causative factors based
on their correlation with the landslide occurrences are shown in Table
3. The LSMN and LSMR maps analysis
indicates that drainage density, TWI, NDVI and distance from road (for
LSMR only) were the critical factors that affect the
study area’s landslide susceptibility. While analysing the hydrological
parameters, the highest FR values were obtained for areas with very high
drainage density (14.7) and very high TWI (49.7). Such areas were found
to be more susceptible to landslides. Also, it was found that areas near
the vicinity of roads are generally more prone to landslides. Further,
the FR values of the distance from the road classes of 0-100 m (6.4) and
100-200 m (5.7) were found to be highest therefore, such areas were
found to be more landslide prone. The study area was categorised into
five NDVI classes: waterbodies, urban area, barren land, shrubs and
grasslands, and sparse and dense vegetation. The areas closer to
waterbodies had the highest FR value (7.7), followed by urban areas
where human interference with natural slopes was observed.
The analysis of CF values indicated a similar trend with classes of very
high drainage density (0.93), very high TWI (0.98), NDVI waterbodies
(0.87) and 0-100 m (0.84) distance from road indicating the highest
correlation with landslide occurrence. Further, the slope gradient
directly relates to landslides occurrence as steeper slopes tend to be
more unstable than flatter terrains. It was interpreted from the data
that slope gradient classes, namely; steep (35°-45°) and very steep
(>45°), had the highest CF values and were more prone to
landslides, whereas no landslides were reported for flat
(<15°) slope gradient class. Similarly, it was observed that
the probability of landslides was moderate at lower elevations
(400m-1000m) due to modest terrain characteristics. The highest
probability of landslides was observed at high elevations (2000m-2500m).
At very high elevations (2500m-3500m), the probability of landslides
again decreases. This might be attributed to lesser reporting of
landslides due to rugged terrain at areas with higher elevations.
Regarding the geological aspects, the Middle Siwalik Group was found to
have the highest CF values (0.74). This group predominantly consists of
medium to coarse grained sandstone and red clay alternation, soft pebble
with subordinate claystone and a locally thick prism of the conglomerate
which might be attributed to its higher landslide susceptibility. During
the analysis of soil classes, the highest CF was obtained for the lesser
Himalayan soils of fluvial valleys (0.75), followed by Siwalik soils of
side and reposed slopes (0.62). Both these soil types are described as
loamy to loamy-skeletal soils, facilitating moderate to severe erosion.
The analysis of the Shanon Entropy model indicated that the highest
Wij values were obtained for drainage density, TWI, NDVI
and distance from road factors and highest Pij values
were attributed to very high drainage density (0.58), very high TWI
(0.42), NDVI waterbodies (0.57) and 0-100 m distance from roads (0.35).
Hence these factors had the highest influence on landslide occurrence.
Along with these, the areas with steep (35°-45°) and very steep
(>45°) slopes and moderately high elevation class was found
to have the highest Pij values and had a moderate
influence on landslides occurrence. The geology map analysis again
confirms that the Middle Siwalik Group, with the highest
Pij value (0.15), was highly prone to landslides,
followed by the Dharmasala Group. Similarly, soil classes analysis
confirms that the fluvial valley soils of lesser Himalayas with the
highest Pij value (0.70) were highly prone to landslides
because of their shallow depth and excessive drainage characteristics.
All other landslide causative factors such as aspect, curvature,
lineament density etc. and their classes with the highest FR, CF and SE
values had low to moderate influence on landslides occurrence.
4.3 Accuracy Assessment and Validation of
Models
In this study, the LSMN and LSMR maps
prepared using FR, CF and SE models were evaluated for accuracy of
prediction and validation using ROC curves and AUC technique. These are
well known techniques to determine the quality of a statistical model by
plotting the fraction of true positives values out of total positives
values and false positives values out of total negatives values by
determining Sensitivity and Specificity (Devkota et al., 2013; Nohani et
al., 2019). Each model’s prediction rate curve was plotted using
training data set of 1199 landslides (70%), and the validation rate
curve of each model was plotted using a validation data set of 524
landslides (30%). The relative ROC curves of the three models are shown
in Figure 6. Based on the ROC results and AUC evaluation, all three
models offer the satisfactory prediction and validation accuracy.
However, the Shanon Entropy model was found to have the most accurate
prediction and validation for landslide susceptibility mapping of
LSMN and LSMR maps.