Spatial Flood Vulnerability Assessment in Peninsular Malaysia using Data Envelopment Analysis (Dea)

Abstract

Flood has caused an enormous negative impact on the environment and the population safety in Malaysia. Many areas are found to be vulnerable to flood due to heavy rainfall during monsoon seasons. However, not many studies were done to identify how vulnerable the prone areas are affected. This study focused on developing flood vulnerability measurement in Peninsular Malaysian states. Data Envelopment Analysis (DEA) was applied on a set of secondary data consisting of several input and output variables across 11 years from 2004 to 2014. The flood vulnerability index for each dimension was computed based on three aspects of flood vulnerability dimensions, i.e. the Population Vulnerability, the Social Vulnerability and the Biophysical Vulnerability. The result showed that Johor was the most vulnerable state among all the states in Peninsular Malaysia in terms of the Population Vulnerability. Meanwhile, Kelantan was the most vulnerable state in the Social Vulnerability and Kedah was the most vulnerable state in the Biophysical Vulnerability. The assessment of flood vulnerability can provide multi-information that may well contribute to a deeper understanding of flood disaster scenario in Malaysia.

A review on methodologies adopted during environmental impact assessment of development projects

“…Currently the visualization tool such as Weighted Linear Combination methods in Geographic Information System (GIS) and Moderate Resolution Imaging Spectro radiometer (MODIS) were commonly approaches used in flood vulnerability assessment but very sensitive to weights of sub-indices which the calculation of weighting depends on arbitrary decisions which will reduce the applicability, which can be placed in such weighting methods (Wei et al, 2004) [122] . The assessment of flood vulnerability could provide multi-information about flood disaster scenario in Malaysia studied by Saharizan et al (2018) [104] . Data Envelopment Analysis (DEA) was a powerful approach to measure efficiency of decision making units (DMUs) with a set of input and output variables (Khodabakhshi & Asgharian, 2009) [61] .…”