Articles | Volume 24, issue 3
https://doi.org/10.5194/nhess-24-1051-2024
https://doi.org/10.5194/nhess-24-1051-2024
Research article
 | 
28 Mar 2024
Research article |  | 28 Mar 2024

Anticipating a risky future: long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

Christian Geiß, Jana Maier, Emily So, Elisabeth Schoepfer, Sven Harig, Juan Camilo Gómez Zapata, and Yue Zhu

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Revised manuscript under review for NHESS
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Cited articles

Abar, S., Theodoropoulos, G. K., Lemarinier, P., and O'Hare, G. M. P.: Agent Based Modelling and Simulation tools: A review of the state-of-art software, Computer Science Review, 24, 13–33, https://doi.org/10.1016/j.cosrev.2017.03.001, 2017. 
Aggarwal, C. C.: Neural Networks and Deep Learning: A Textbook, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-94463-0, 2018. 
Androsov, A., Harig, S., Zamora, N., Knauer, K., and Rakowsky, N.: Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima/Callao, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1365, 2023. 
Calderon, A. and Silva, V.: Exposure forecasting for seismic risk estimation: Application to Costa Rica, Earthq. Spectra, 37, 1806–1826, https://doi.org/10.1177/8755293021989333. 
Chen, Y., Li, X., Huang, K., Luo, M., and Gao, M.: High-Resolution Gridded Population Projections for China Under the Shared Socioeconomic Pathways, Earths Future, 8, e2020EF001491, https://doi.org/10.1029/2020EF001491, 2020. 
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We establish a model of future geospatial population distributions to quantify the number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru, for the year 2035. Areas of high earthquake intensity will experience a population growth of almost 30 %. The population in the tsunami inundation area is estimated to grow by more than 60 %. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.
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