TITLE:
ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition
AUTHORS:
Cassiano Antonio Bortolozo, Luana Albertani Pampuch, Marcio Roberto Magalhães De Andrade, Daniel Metodiev, Adenilson Roberto Carvalho, Tatiana Sussel Gonçalves Mendes, Tristan Pryer, Harideva Marturano Egas, Rodolfo Moreda Mendes, Isadora Araújo Sousa, Jenny Power
KEYWORDS:
Landslides Early Warning System (LEWS), Cluster Analysis, Landslides, Brazil
JOURNAL NAME:
International Journal of Geosciences,
Vol.15 No.1,
January
31,
2024
ABSTRACT: A 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.