Landslide vulnerability and risk assessment for multihazard scenarios using airborne laser scanning data

Landslides are one of the many forms of natural hazards that often cause severe property damages, economic loss, and high maintenance costs. Slope failures are a result of multiple triggering factors, including anthropogenic activities, earthquakes, and intense rainfall, and reactions of a host of unstable surface materials related to geology, land cover, slope geometry, moisture content, and vegetation. In recent decades, numerous people have become the victims of landslides in many regions worldwide. Although there has been a broad exploration into measuring landslide hazard, research into outcome investigation and the appraising of the vulnerability has been constrained and remains in its infancy. An understanding and assessment of the vulnerability of elements exposed to landslide hazard are of key importance to landslide risk assessment. This study presents a semi-quantitative landslide vulnerability and risk assessment for the hazard mapping of rainfall-induced landslides. This approach was tested in the Ringlet area in Malaysia. This research has three objectives; the first objective focuses on construction of landslide susceptibility map using conditioning factors and probability models for the study area. The logistic regression model was employed. The most significant landslide conditioning factors were prioritized, and the model was validated using success and prediction rate curves. The predicted map yielded higher prediction accuracy and achieved better discrimination of susceptible zones. The second objective focuses on developing hazard assessment by implementing the temporal probability. Using available precipitation data from 2000 to 2014. Four different antecedent values: average value of any day in the year, and abnormal intensity in the day. And three different average rainfall depth: 5, 10, and 15 years. Finally, hazard maps were developed based on the multiplied results of the spatial and temporal of Ringlet area. In this study the semi-quantitative risk assessment of landslide hazards and vulnerability map was developed. An integration between the vulnerability and the hazard maps were accomplished to predict the facilities that are likely to be affected by direct risks. Additionally, an exposure overlay of elements at risk and hazard maps for different duration of intensity were employed to calculate the loss. Results then used to predict area under risk and calculate annualized risk. The expected results proved the capacity of the proposed methods to make valid prediction under landslide risk conditions in a data-scarce environment. The results are expected not only provide an assessment of future landslide hazards and risks but also serve as a guide for land use planners. The presented methods and information will add a valuable contribution to the landslide hazard and risk assessment at medium scale data analysis.

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