Analysis of human exposure to landslides with a GIS multiscale approach

S. Modugno (), S. C. M. Johnson, P. Borrelli, E. Alam, N. Bezak and H. Balzter
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S. Modugno: United Nation World Food Programme
S. C. M. Johnson: University of Leicester
P. Borrelli: University of Pavia
E. Alam: Rabdan Academy
N. Bezak: University of Ljubljana
H. Balzter: University of Leicester

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 112, issue 1, No 17, 387-412

Abstract: Abstract Decision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.

Keywords: Disaster risk reduction; Landslide probability; Logistic regression; Landslide trigger factors; GIS model; Global map (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11069-021-05186-7

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