Sirio Modugno (Avtor), Sarah C. M. Johnson (Avtor), Pasquale Borrelli (Avtor), Edris Alam (Avtor), Nejc Bezak (Avtor), Heiko Balzter (Avtor)

Povzetek

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.

Ključne besede

zmanjševanje tveganja;plazovi;regresija;sprožitveni dejavniki;GIS;globalna karta;disaster risk reduction;landslide probability;logistic regression;landslide trigger factors;GIS model;global map;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FGG - Fakulteta za gradbeništvo in geodezijo
UDK: 502/504:55
COBISS: 93500419 Povezava se bo odprla v novem oknu
ISSN: 0921-030X
Št. ogledov: 101
Št. prenosov: 19
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: zmanjševanje tveganja;plazovi;regresija;sprožitveni dejavniki;GIS;globalna karta;
Vrsta dela (COBISS): Članek v reviji
Strani: [26] f.
Zvezek: ǂVol. ǂ10. jan.
Čas izdaje: 2022
DOI: 10.1007/s11069-021-05186-7
ID: 14768494
Priporočena dela:
, magistrsko delo
, diplomsko delo Visokošolskega strokovnega študijskega programa I. stopnje Strojništvo
, delo diplomskega seminarja