Landslides represent one of the world’s most dangerous and widespread risks, annually causing thousands of deaths and billions of dollars worth of damage. Building on and around hilly areas in many regions has increased, and it poses a severe threat to the physical infrastructure and people living within such zones. Quantitative assessment of social vulnerability in Malaysia is worrying because it has been given less attention than hazard-related studies. Therefore, this study’s objective is to find out the indicators used for social vulnerability assessment in the context of a landslide in Malaysia. The analysis is critical for understanding the measures of social vulnerability, given that the incorporation of climate change and disaster risk mitigation issues in urban planning and management are considered priorities in ensuring a stable population growth and avoiding economic disruption. A systematic study on the Scopus and Web of Science repositories was conducted based on the PRISMA Report analysis method. This article concluded that there are six important indicators of social vulnerability in the context of landslide in Malaysia.
A novel approach to consider local‐scale defence infrastructure in an urban environment, coupled with a broadscale hydraulic model framework, is applied to the capital city of Kuala Lumpur, Malaysia. Broadscale hydraulic modelling frameworks are often able to employ more complex models, but are typically limited to homogenous decision‐making to ensure standardised outputs across large regions. Conversely, small‐scale hydraulic modelling frameworks tend to better integrate local‐scale features but can be computationally expensive to scale up beyond a regional view. Improvements to the broadscale hydraulic model framework through the incorporation of defence systems yield a more accurate representation of fluvial flood risk. This study incorporates defences in Kuala Lumpur, yielding a reduction in our estimates of fluvial flood extent by around 40%. The results of this study are validated against a set of high‐quality observations, demonstrating the capability of the model framework in capturing flood risk in more than 95% of known flood risk zones in the city. Incorporating defence infrastructure using data‐driven decision making and existing functionality in the hydraulic model could be automated in future model builds. This new approach bridges the gap between local‐scale model frameworks and the broadscale, homogenous 2D hydraulic modelling studies.
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