Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard.

Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression / Chalkias, C.; Polykretis, C.; Karymbalis, E.; Soldati, M.; Ghinoi, A.; Ferentinou, M.. - In: BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT. - ISSN 1435-9529. - 79:6(2020), pp. 2799-2814. [10.1007/s10064-020-01733-x]

Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression

Soldati M.;
2020

Abstract

Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard.
6-feb-2020
79
6
2799
2814
Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression / Chalkias, C.; Polykretis, C.; Karymbalis, E.; Soldati, M.; Ghinoi, A.; Ferentinou, M.. - In: BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT. - ISSN 1435-9529. - 79:6(2020), pp. 2799-2814. [10.1007/s10064-020-01733-x]
Chalkias, C.; Polykretis, C.; Karymbalis, E.; Soldati, M.; Ghinoi, A.; Ferentinou, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1207375
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