the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cost estimation for the monitoring instrumentalization of Landslide Early Warning Systems
Mortiz Gamperl
Marlene Kühnl
Carolina Garcia-Londoño
John Singer
Hannes Taubenböck
Abstract. Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in high-hazard areas increases the associated risks. Early warning systems (EWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study aims to develop a cost-effective method for low-cost and easy-to-use EWS instrumentalization in landslide-prone areas identified based on data-driven methods. We exemplify this approach in the landslide-prone city of Medellín, Colombia. We introduce a workflow to enable decision-makers to balance financial costs and the potential to protect exposed populations. To achieve this, we first mapped city-level landslide susceptibility using data on hazard levels, landslide inventories, geological and topographic factors using a random-forest model. We then combine the landslide susceptibility map with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of EWS-monitoring sensors at the selected sites, using lessons learned from a pilot EWS in Bello Oriente, a neighbourhood in Medellín. Our study estimates that EWS monitoring sensors could be installed in several landslide-prone areas in the city of Medellín with a budget ranging from €5 to €41 per person (roughly COP 23,000 to 209,000), improving the resilience over 190,000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, they are a social group of very high vulnerability. We provide recommendations for stakeholders on where to proceed with EWS instrumentalization based on five different cost-effective scenarios. Finally, we discuss the limitations, challenges, and opportunities for the successful implementation of an EWS. This approach enables decision-makers to prioritize EWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods.
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Marta Sapena et al.
Status: open (until 26 Apr 2023)
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RC1: 'Comment on nhess-2023-41', Anonymous Referee #1, 26 Mar 2023
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The manuscript entitled “Cost estimation for the monitoring instrumentalization of Landslide Early Warning Systems” develops a cost-effective method for low-cost and easy-to-use EWS instrumentalization in landslide-prone areas identified based on data-driven methods. In general, the manuscript contains an interesting topic that is considered one of the important stages in landslide mechanism assessment; but there are several modifications that have to be considered. In this regard, the following comments are requested to be addressed by the authors:
C1: The English of the paper is readable; however, I would suggest the authors have it checked, preferably by a native English-speaking person, to avoid any mistakes.
C2: The necessity & novelty of the manuscript should be presented and stressed in the “Introduction” section.
C3: Provide a literature of the methods developed/applied on landslide mechanism assessment and modeling in “Introduction”. The use of a table to demonstrate the advantage-disadvantage of these methods can be useful. Towards the end, mention the superiority & repeat the novelty of your work.
C4: Please add a subsection clearly articulating the main limitations, wider applicability of your methods, and findings in the “Discussion” section.
C5: The authors should deepen the discussion.
C6: As a suggestion, the following articles could be useful for improving this manuscript.
- Nikoobakht, S., Azarafza, M., Akgün, H., & Derakhshani, R. (2022). Landslide susceptibility assessment by using convolutional neural network. Applied Sciences, 12(12), 5992. https://doi.org/10.3390/app12125992
- Fathani, T.F., Karnawati, D., Wilopo, W., Setiawan, H. (2023). Strengthening the Resilience by Implementing a Standard for Landslide Early Warning System. In: Sassa, K., Konagai, K., Tiwari, B., Arbanas, Ž., Sassa, S. (eds) Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022. Progress in Landslide Research and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-16898-7_20
- Nanehkaran, Y. A., Licai, Z., Chengyong, J., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. (2023). Comparative Analysis for Slope Stability by Using Machine Learning Methods. Applied Sciences, 13(3), 1555. https://doi.org/10.3390/app13031555
- Yang, F.-Y.; Zhuo, L.; Xiao, M.-L.; Xie, H.-Q.; Liu, H.-Z.; He, J.-D. A Statistical Risk Assessment Model of the Hazard Chain Induced by Landslides and Its Application to the Baige Landslide. Appl. Sci.2023, 13, 3577. https://doi.org/10.3390/app13063577
- Gariano, S.L., Melillo, M., Brunetti, M.T., Kumar, S., Mathiyalagan, R., Peruccacci, S. (2023). Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India. In: Sassa, K., Konagai, K., Tiwari, B., Arbanas, Ž., Sassa, S. (eds) Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022. Progress in Landslide Research and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-16898-7_27
- Segoni, S., Serengil, Y. & Aydin, F. A prototype landslide early warning system in Rize (Turkey): analyzing recent impacts to design a safer future. Landslides20, 683–694 (2023). https://doi.org/10.1007/s10346-022-01988-3
- Han, Min, et al. "An Early Warning System for Landslide Risks in Ion-Adsorption Rare Earth Mines: Based on Real-Time Monitoring of Water Level Changes in Slopes." Minerals13.2 (2023): 265. https://doi.org/10.3390/min13020265
Citation: https://doi.org/10.5194/nhess-2023-41-RC1
Marta Sapena et al.
Marta Sapena et al.
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