5. Discussion
The process of generation of LSM maps is complex and requires multistep
in depth analysis. This study analyses three main issues: (a) the
mapping of landslide susceptibility of Mandi district based on relevant
landslide causative factors (b) the comparison of three statistical
models, namely Frequency Ratio (FR), Certainty Factor (CF) and Shanon
Entropy (SE) for their accuracies in predicting landslides and (c) the
assessment of landslide susceptibility change due to road construction
in Mandi district.
An appropriate selection of landslide causative factors requires
extensive knowledge of geographical and topographical aspects of the
study area and triggering mechanisms associated with them (Guzzetti et
al., 1999; Costanzo et al., 2012). Generally, the optimal approach is
manual selection based on expert opinion, but there are no universal
guidelines for identification and selection of landslide causative
factors (Dou et al., 2015). Based on this criterion, eleven landslide
causative factors were identified, all of whom had a strong association
with landslide occurrence. Further, the interdependence of these factors
was investigated using a multicollinearity test, which indicated that
the selected factors were independent and credible. Analysis of the
three statistical models results revealed that drainage density,
distance from the road, TWI and NDVI were the most influential factors
for landslide occurrence. At the same time, the slope curvature and
aspect were the least influential factors on landslide occurrence. The
rest of the parameters, such as elevation, lineament density, geology
and soil, had a moderate impact on landslide occurrence. A high density
of drainage networks and steeper slopes, and sedimentary rocks like
medium to coarse grained sandstone and conglomerate can be attributed as
principle factors of landslide occurrence in the study area. These
factors, especially when combined with excessively drained soils, high
lineament density and improper road construction activities, tend to
increase the study area’s landslide susceptibility. Further, the NDVI
map analysis suggested that the areas near waterbodies and the areas
interfered with by human settlements, road construction or any other
infrastructure development activities tend to be more prone to
landslides. On the contrary, regions having very high elevations, a
higher percentage of vegetation and flatter slope gradients have minimum
susceptibility to landslides. These results conform with the findings of
similar research reports. (Dou et al., 2015; Hong and Bui, 2015; Roy,
2019).
Statistical modelling is an essential component in determining the
landslide susceptibility of an area. The accuracy of statistical models
is primarily dependent upon the data quality and model structure. In
this study, three statistical models, namely: FR, CF and SE, were used
to determine the Mandi district’s landslide susceptibility. The
validation of these models was done using ROC curves and the AUC
technique, assuming that landslides were dependent only on the given
spatial parameters with rainfall as the common triggering factor. The
results indicate that all three models have satisfactory values for
prediction and accuracy. Still, the relative contribution of the
landslide causative factors varied with the models, as shown in Figure
6. The highest accuracy of prediction and validation was demonstrated by
Shanon Entropy (83-86%). The SE model is an entropy based data driven
model which directly stores information of variables and correlates with
the probability of landslide occurrence. This might be the reason for
its higher accuracy in comparison to the other two models. The SE model
indicated that drainage density and distance from roads as the two major
contributing factors towards landslide susceptibility. The SE model also
suggested a strong correlation between higher slope gradient and TWI and
landslide occurrence. Some other factors like NDVI, soil, geology and
elevation also indicated significant contribution. The geology
Dharmasala Group, Dagshai and Kasauli Formations combined with soils of
fluvial valleys at moderate to high elevations showed the highest
correlation with landslide occurrence. FR model being on the observation
model had (75-79%) accuracy of prediction and validation. As a
rule-based model, the CF model had a relatively good accuracy of
predicting and validating (75-82%). These models also suggested
drainage density and road construction as two fundamental factors with
maximum impact on landslide susceptibility in the study area. The
comparison of these models was found to be in accordance with recent
studies of landslide susceptibility analysis. (Devkota et al., 2013; Lee
and Pradhan, 2007; Nohani et.al, 2019; Wang et al., 2015).
In this study, the LSMN and LSMR maps
were prepared using FR, CF and SE models. The results of all the models
indicated that the road construction activities in the Mandi district
appear to be a primary factor responsible for an increase in landslide
susceptibility of the study area. For comparison, ten common landslide
causative factors were considered for preparing two susceptibility maps.
The additional factor of the distance from the road was only considered
for the LSMR map. These susceptibility maps are further
classified into five zones of susceptibility: very low, low, moderate,
high and very high susceptibility. The analysis of change in
susceptibility was done by comparing each class’s percentages in both
LSMN and LSMR maps, as shown in Table 4.
The analysis of LSMN and LSMR maps of FR
model indicates that the percentage of area in high susceptibility zone
increases from 22.9% in LSMN map to 24.5% in
LSMR map and the percentage of area in very high
susceptibility zone increases from 13.2% in LSMN map to
15.8% in LSMR map.
Similarly, for the CF model, the high susceptibility zone area increases
from 23% in the LSMN map to 24% in the
LSMR map. The area is very high susceptibility zone
increases from 7% in LSMN map to 8% in
LSMR map. Likewise, in the SE model having the highest
prediction accuracy, it was observed that the percentage of area in high
susceptibility zone increases from 19.3% in LSMN map to
21.6% in LSMR map, and the percentage of area in very
high susceptibility zone increases from 10.8% in LSMNmap to 12.5% in LSMR map. It can be observed from the
LSMR maps in Figure 5. that the areas in the vicinity of
the road, particularly in classes (0-100m) and (100-200m), witnessed an
increase in landslide susceptibility. This can be attributed to the fact
that the cutting and tempering of natural bed slope for road
construction increases the risk of slope failure in that area.
Additionally, road construction may change or block the natural drainage
networks operating in mountainous terrains. This might further increase
the probability of landslide occurrence in that area.