Ingenieur Vol.79 July-Sept 2019 ingenieur 2019 july-sept | Page 34

INGENIEUR Relevant element-at-risk characteristics (for example types, materials, dimensions and locations) are essential for detailed classification of each element-at-risk. Each element-at-risk is classified into its sub-classification scheme based on its main characteristics. This information is combined with the landslide hazard index as a primary input for expert-based vulnerability assessments. METHODOLOGY Figure 2.1 shows the overall methodology which is divided into eleven stages as follows: The overall method of assessing and developing the parameters/indicators of landslide vulnerability assessment and risk index of critical infrastructures can be divided into four main stages namely 1) data acquisition and pre- processing of geospatial data; 2) improvements in landslide vulnerability clusters, indicators, sub-indicators and weight values; 3) landslide vulnerability and risk mapping in Cameron Highlands and; 4) evaluation of the landslide vulnerability and risk assessment methodology. The first stage focused on the data acquisition that included geospatial and non-geospatial data. The geospatial data included acquisition of high-resolution aerial photos at Ringlet and Lembah Bertam, Cameron Highlands. The aerial photos were processed to produce digital terrain models (DTMs), digital surface models (DSMs) and orthophotos of the study areas. In addition, several other ancillary data were obtained from different agencies, for example: landslide hazard maps, high resolution DTMs and orthophotos from the Mineral and Geoscience Department (JMG). Finally, the input information for the proposed landslide vulnerability and risk assessment such as initial information on the clusters (C, E, I, and P), indicators, sub-indicators and weights were obtained via intensive literature reviews. The second stage focused on improvements of landslide vulnerability clusters, indicators, sub-indicators and weight values. Several focus group discussions (FGDs) were conducted with stakeholders and internal experts to improve the landslide vulnerability and risk assessment methods. The first FGD was conducted with 6 32 VOL VOL 79 55 JULY-SEPTEMBER JUNE 2013 2019 different stakeholders. The FGD involved detailed explanation on the concept of landslide vulnerability and risk assessment which included step-by-step explanation on the procedure used in determining the clusters, indicators, sub- indicators and weight values. Each participant was required to fill in a specially designed survey form for landslide vulnerability and risk assessments. The outcomes from this FGD were further improved with specific FGDs with a group for a dam and TNB powerlines. Finally, all the input for the landslide vulnerability and risk assessment of each CI and landslide type was evaluated and finalised by the internal experts of the consultant group. Several analyses were carried out to determine the consistency of inputs from stakeholders, the sensitivity of each indicator and cluster, and the reliability of the vulnerability index based on simulation of different landslide vulnerability scenarios (worst, medium and best case scenarios). The consistency analysis was aimed at analysing the consistency of weight values assigned by the stakeholders for the indicators and sub-indicators through the analysis of the standard deviation of weight values between participants. The sensitivity analysis focused on analysing the sensitivity of each indicator and sub- indicator towards the estimation of the landslide vulnerability value (index) based on the one-at-a- time (OAT) method. The simulations on the other hand analysed the reliability of weight values given by the stakeholders and internal experts (for each CI and landslide type). The best case landslide scenario was expected to produce the lowest vulnerability value that can be classified as a “very low vulnerability” class. The medium case landslide scenario was expected to produce a medium vulnerability value that can be classified as a “moderate vulnerability” class. Finally, the worst case landslide scenario was expected to produce the highest vulnerability value that can be classified as a “very high vulnerability” class. The landslide vulnerability and risks were grouped into five classes namely, very high, high, moderate, low and very low landslide risk areas. The landslide vulnerability map was classified using the same five classes. The landslide risk map was produced based on the matrix combination of landslide vulnerability and hazard classes. The landslide hazard map of the study