Mohamed Daud

Inquiries into Malaysia's socio‐technical disasters: recommendations and lessons learnt

Most democratic countries hold inquiries into disasters. One of their key functions is to establish the cause of an event and to learn lessons in order to prevent a recurrence. In addition, they offer an opportunity for communal catharsis, permitting the public to vent anger, distress and frustration and to exert pressure for policy changes. Malaysia has experienced six landmark socio-technical disasters since 1968, which resulted in the proposal or amendment of various safety/emergency acts and regulations. The authors used a grounded theory approach utilising a constant comparative method to analyse the recommendations made by the inquiries into these events. Data indicate that social and technical recommendations comprise 85 and 15 per cent, respectively, of the total recommendations made by the inquiry committees. This paper offers suggestions for improving the management of inquiry tribunals, as they will remain a valuable source of information for society and corporations to learn from past incidents.

Landslide Susceptibility Analysis Using An Artificial Neural Network Model

This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.