Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

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dc.contributor.authorMaragaño-Carmona, Gonzalo
dc.contributor.authorFustos Toribio, Ivo J.
dc.contributor.authorDescote, Pierre-Yves
dc.contributor.authorRobledo, Luis F.
dc.contributor.authorVillalobos, Diego
dc.contributor.authorGatica, Gustavo
dc.date.accessioned2023-10-17T13:40:30Z
dc.date.available2023-10-17T13:40:30Z
dc.date.issued2023-07
dc.descriptionIndexación: Scopus.es
dc.description.abstractThe determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.es
dc.description.urihttps://www-scopus-com.recursosbiblioteca.unab.cl/record/display.uri?eid=2-s2.0-85166202333&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=acc61d57238ef2b32a8ffac8bb4c85c6&sot=aff&sdt=cl&cluster=scofreetoread%2c%22all%22%2ct&sl=34&s=AF-ID%2860002636%29+AND+SUBJAREA%28BIOC%29&relpos=20&citeCnt=0&searchTerm=
dc.identifier.citationWater (Switzerland) Open Access Volume 15, Issue 14 July 2023 Article number 2514es
dc.identifier.doi10.3390/w15142514
dc.identifier.issn20734441
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/53480
dc.language.isoenes
dc.publisherMDPIes
dc.rights.licenseCC BY 4.0 DEED
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCentral Andeses
dc.subjectLandslide susceptibilityes
dc.subjectLogistic modelses
dc.subjectRainfall-induced landslidees
dc.subjectSusceptibility temporal variationses
dc.titleRainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Casees
dc.typeArtículoes
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