ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

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dc.contributor.authorBortolozo, Cassiano Antonio
dc.contributor.authorPampuch, Luana Albertani
dc.contributor.authorAndrade, Marcio Roberto Magalhães de
dc.contributor.authorMetodiev, Daniel
dc.contributor.authorCarvalho, Adenilson Roberto
dc.contributor.authorMendes, Tatiana Sussel Gonçalves
dc.contributor.authorPryer, Tristan
dc.contributor.authorEgas, Harideva Marturano
dc.contributor.authorMendes, Rodolfo Moreda
dc.contributor.authorSousa, Isadora Araújo
dc.contributor.authorPower, Jenny
dc.date.accessioned2025-02-27T14:24:42Z
dc.date.available2025-02-27T14:24:42Z
dc.date.issued22024
dc.description.abstractA significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
dc.description.physical16 p.
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFinanciadora de Estudos e Projetos (Finep)
dc.description.uriCNPQ 426530/2018-7; FINEP (FINEP/FNDCT 01/2016)
dc.format.mimetypePDF
dc.identifier.affiliationCentro Nacional de Monitoramento e Alertas de Desastres Naturais
dc.identifier.affiliationInstituto de Ciências e Tecnologia
dc.identifier.affiliationUniversity of Bath
dc.identifier.bibliographicCitationBortolozo, Cassiano Antonio. et al. ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition. International Journal of Geosciences, v. 15, n. 1, p. 54-69, 2024.
dc.identifier.doi10.4236/ijg.2024.151005
dc.identifier.urihttps://repositorio.univap.br/handle/123456789/604
dc.language.isoen_US
dc.publisherScientific Research Publishing
dc.rights.holderCassiano Antonio Bortolozo
dc.rights.holderLuana Albertani Pampuch
dc.rights.holderMarcio Roberto Magalhães de Andrade
dc.rights.holderDaniel Metodiev
dc.rights.holderAdenilson Roberto Carvalho
dc.rights.holderTatiana Sussel Gonçalves Mendes
dc.rights.holderTristan Pryer
dc.rights.holderHarideva Marturano Egas
dc.rights.holderRodolfo Moreda Mendes
dc.rights.holderIsadora Araújo Sousa
dc.rights.holderJenny Power
dc.subject.keywordLandslides Early Warning System
dc.subject.keywordCluster Analysis
dc.subject.keywordLandslides
dc.titleARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition
dc.typeArtigos de Periódicos

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