Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3913-2023
https://doi.org/10.5194/nhess-23-3913-2023
Research article
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24 Dec 2023
Research article | Highlight paper |  | 24 Dec 2023

Cost estimation for the monitoring instrumentation of landslide early warning systems

Marta Sapena, Moritz Gamperl, Marlene Kühnl, Carolina Garcia-Londoño, John Singer, and Hannes Taubenböck

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Inform@Risk. The Development of a Prototype for an Integrated Landslide Early Warning System in an Informal Settlement: the Case of Bello Oriente in Medellín, Colombia
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Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-53,https://doi.org/10.5194/nhess-2023-53, 2023
Revised manuscript accepted for NHESS
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Cited articles

Abu El-Magd, S. A., Ali, S. A., and Pham, Q. B.: Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain, Earth Sci. Inform., 14, 1227–1243, https://doi.org/10.1007/s12145-021-00653-y, 2021. 
Ado, M., Amitab, K., Maji, A. K., Jasiñska, E., Gono, R., Leonowicz, Z., and Jasiñski, M.: Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey, Remote Sens., 14, 3029, https://doi.org/10.3390/rs14133029, 2022. 
Alcaldía de Medellín: Plan de ordenamiento territorial, Acuerdo 48 de 2014, Medellín, 2014a. 
Alcaldía de Medellín: Revisión y ajuste del Plan de Ordenamiento Territorial de Medellín, Evaluación y Seguimiento – Tomo IIIB, Versión 3-Concertación con Autoridades Ambientales, Medellín, 2014b. 
Alcaldía de Medellín: GeoMedellín, https://www.medellin.gov.co/geomedellin/ (last access: 4 July 2023), 2023. 
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Executive editor
Justification of handling editor: The paper describes a methodology for designing an early-warning for landslides based in open source low cost instruments. This will be of interest to wider audiences since it is at the interface of earth sciences, Internet of Things (IOT), wireless sensor networks, and communications.
Short summary
A new approach for the deployment of landslide early warning systems (LEWSs) is proposed. We combine data-driven landslide susceptibility mapping and population maps to identify exposed locations. We estimate the cost of monitoring sensors and demonstrate that LEWSs could be installed with a budget ranging from EUR 5 to EUR 41 per person in Medellín, Colombia. We provide recommendations for stakeholders and outline the challenges and opportunities for successful LEWS implementation.
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