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Título : Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador
Autor : Puente-Sotomayor, Fernando
Mustafa, Ahmed
Teller, Jacques
Palabras clave : Landslide susceptibility
Sensitivity analysi
Kolmogorov-Smirnov test
Andean cities
Fecha de publicación : 20-ago-2021
Citación : Puente-Sotomayor, F., Mustafa, A. & Teller, J. Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador. Geoenviron Disasters 8, 19 (2021). https://doi.org/10.1186/s40677-021-00184-0
Resumen : Although the Andean region is one of the most landslide-susceptible areas in the world, limited attention has beendevoted to the topic in this context in terms of research, risk reduction practice, and urban policy. Based on thecollection of landslides data of the Andean city of Quito, Ecuador, this article aims to explore the predictive powerof a binary logistic regression model (LOGIT) to test secondary data and an official multicriteria evaluation model forlandslide susceptibility in this urban area. Cell size resampling scenarios were explored as a parameter, as theinclusion of new“urban”factors. Furthermore, two types of sensitivity analysis (SA), univariate and Monte Carlomethods, were applied to improve the calibration of the LOGIT model. A Kolmogorov–Smirnov (K-S) test wasincluded to measure the classification power of the models. Charts of the three SA methods helped to visualize thesensitivity of factors in the models. The Area Under the Curve (AUC) was a common metric for validation in thisresearch. Among the ten factors included in the model to help explain landslide susceptibility in the context ofQuito, results showed that population and street/road density, as novel“urban factors”, have relevant predictingpower for landslide susceptibility in urban areas when adopting data standardization based on weights assigned byexperts. The LOGIT was validated with an AUC of 0.79. Sensitivity analyses suggested that calibrations of the best-performance reference model would improve its AUC by up to 0.53%. Further experimentation regarding othermethods of data pre-processing and a finer level of disaggregation of input data are suggested. In terms of policydesign, the LOGIT model coefficient values suggest the need for a deep analysis of the impacts of urban features,such as population, road density, building footprint, and floor area, at a household scale, on the generation oflandslide susceptibility in Andean cities such as Quito. This would help improve the zoning for landslide riskreduction, considering the safety, social and economic impacts that this practice may produce
URI : http://www.dspace.uce.edu.ec/handle/25000/24456
ISSN : 2197-8670
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