INGENIEUR
area was obtained from the Department of
Mineral and Geoscience, which was produced
using high resolution remote sensing and
geospatial modelling approaches. The landslide
map was already classified in similar classes.
Finally, the risk map was produced by crossing
both vulnerability and hazard maps and classified
into the same five classes.
Suggested Indicators for Landslide Vulnerability
Assessment
The proposed landslide vulnerability assessment
requires determination of four groups of indicators
i.e. susceptibility of critical infrastructures (C), the
effect of surrounding environment or mitigation
measures (E), susceptibility of people inside
residential buildings (P) and the intensity of
landslide hazards (I) (Equation 1). In this project
each group indicator was treated equally, in
which all the groups of indicators have the same
influence towards the vulnerability value. In this
case each group was given a weight value of
25% (or 0.25). Initially the weight value was given
equally among the indicators under each group
or differently based on their level of importance
in vulnerability estimation. The weight for each
indicator was then adjusted based on intensive
discussions with the stakeholders. As a result,
different sets of indicators and weight values were
determined for different types of typical landslides
in Malaysia i.e. rotational landslides, translational
landslides, rockfalls and debris flow. In this
project we have proposed suitable indicators
for the different types of landslides and critical
infrastructures.
Focus Group Discussions for Landslide
Vulnerability Assessments
Qualitative methods developed by FGDs were
used for assessing expert inputs of landslide
vulnerability and determination on risk indices for
critical infrastructures. A combination of remotely
sensed data, field data and expert inputs provided
crucial input for the development of methodologies
for the assessment and estimated vulnerability
index for critical infrastructures. A series of FGDs
with Malaysian technical agencies on Landslide
Vulnerability Assessments and the Development
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of Risk Indices for critical infrastructures in
Malaysia was held on October 18, 2018 and
November 8, 2018.
The FGDs were conducted with 23 experts
from 11 agencies and departments (stakeholders)
to explore their views, including a second FGD held
at the Construction Research Institute of Malaysia
(CREAM) on November 8, 2018 that focused on
only one critical infrastructure. The discussion
was held separately for each critical infrastructure
group (Building, Residential and Road, Dam &
Water Treatment, and Utility) to obtain information
on each category’s specific needs. The groups
were shown the information provided in Table 2.1.
In addition, a specific FGD for dams was held on
November 8, 2018 at CREAM. The results of this
specific FGD were used to improve the results
of the previous FGDs. Further discussions of the
results of the FGD that involved internal experts
from different fields were held to improve the
indicators and the weight values of landslide
vulnerability assessments.
The FGDs began with several briefings on
the concept of landslide vulnerability and risk
assessments. The experts were given a clear
step by step instruction on how to fill out the
landslide vulnerability survey forms for each CI
and landslide type. In the first step, the panel
was required to select the type of landslide and
CI. Based on the selected landslide type and CI,
the panels were required to define the related
indicators for each cluster i.e. C, E, I and P. In this
study the C, E, I and P factors are treated equally
strong in landslide vulnerability estimation and
each factor carried 25% of the total weight. In the
third stage panels were required to determine the
score value for each indicator on a scale from 1
to 10. Indicators with a score close to 1 are on
a less important level in landslide vulnerability
compared with indicator scores close to 10
which are more important in the vulnerability
assessment. In this study, the score value for
each indicator (i) was converted to the weight
value based on Equation 1.
Equation (1)