Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima/Callao

Androsov, Alexey; Harig, Sven; Zamora, Natalia; Knauer, Kim; Rakowsky, Natalja

doi:10.5194/egusphere-2023-1365

Cited by 3 publications

(2 citation statements)

References 29 publications

(41 reference statements)

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“…The simulations were generated using the physical generation and propagation model TsunAWI (Harig et al, 2008), which accounts for a triangular mesh with variable resolution as proposed by Harig et al (2020). The available outputs including the maximum tsunami amplitude, arrival times and tsunami inundation depth are displayed (Rakowsky et al, 2013;Androsov et al, 2023). Some of these scenario-based tsunami inundation maps are available in Harig and Rakowsky (2021), respectively.…”

Section: Web Services and Workflow Controlmentioning

confidence: 99%

Between global risk reduction goals, scientific-technical capabilities and local realities: a novel modular approach for multi-risk assessment

Schoepfer,

Lauterjung,

Riedlinger

et al. 2023

Preprint

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Abstract. We live in a rapidly changing and globalized society. The increasing connectivity of our economic, social and technical systems, growing urbanization and the consequences of climate change might lead to more complex risks and multi-dimensional vulnerability. The complex relationships between multiple and consecutive natural hazards, exposed population and built environment result in a variety of cascading eﬀects which, if are often not considered appropriately by decision makers, can result to inadequate or even misleading risk management strategies. Thus, hindering eﬀicient prevention and mitigation measures, and ultimately undermining the resilience of societies. International efforts to identify global risks such as within the Sustainable Development Goals, the Sendai Framework for Disaster Risk Reduction 2015–2030 or the Paris Agreement help to raise awareness, set priorities and face such global problems. However, even if the formulated goals for risk reduction are comprehensible in their abstraction, decision-makers still face major challenges when it comes to local implementation. In this paper, we present a conceptual approach for multi-risk assessment which was designed to serve potential users like disaster risk managers, urban planners or operators of critical infrastructures to increase their capabilities. Based on recent scientific and technical capabilities, we developed a tool through an iterative participative approach which has allowed users to explore various scenarios of multiple hazards, cascading effects and their impacts. As an illustrative example, the experiences during this stimulating process are documented for Lima Metropolitan area (Peru), a megacity exposed to various natural hazards, among them, earthquakes and tsunamis. We believe that such an approach for exploring, describing and quantifying different What-if scenarios can constitute a valuable approach for understanding complex multi-risk processes, preparing for such situations, and serving as a good practice that can be replicated for other areas of interest in the future.

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Section: Web Services and Workflow Controlmentioning

confidence: 99%

Between global risk reduction goals, scientific-technical capabilities and local realities: a novel modular approach for multi-risk assessment

Schoepfer,

Lauterjung,

Riedlinger

et al. 2023

Preprint

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show abstract

“…The ground motion fields are generated 190 based on ground motion prediction equations according to Montalva et al (2017). The tsunami simulations (Androsov et al, 2023) are based on parameters proposed by Jimenez et al (2013). The two data sets are provided with 1-kilometer and 10meter spatial resolution, respectively, and were resampled to the spatial resolution of 100 meters of the population grid for the exposure analysis.…”

Section: Hazard Models: Earthquake and Tsunami 185mentioning

confidence: 99%

Anticipating a risky future: LSTM models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

Geiß

Maier

et al. 2023

Preprint

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Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open source basis globally, and implement machine learning models tailored for time series analysis, i.e., Long Short-Term Memory-based (LSTM) networks, for prediction of future time steps. In detail, we harvest WorldPop population data and learn LSTM and Convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms and derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and Random Forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data. The best trained model is deployed for anticipation of population along a three-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 , the population will experience a growth of almost 30 % over the forecasted time span which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area will grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policy makers to develop effective risk mitigation strategies.

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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

Geiß,

Maier,

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open-source basis globally, and implement machine learning models tailored for time series analysis, i.e., based on long short-term memory (LSTM) networks, for prediction of future time steps. Specifically, we harvest WorldPop population data and teach LSTM and convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms, which are derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and random forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data; the most accurate prediction obtained with an LSTM equipped with a bidirectional learning scheme features a root-mean-squared error of 3.63 people per 100 × 100 m grid cell while maintaining an excellent model fit (R2= 0.995). We deploy this model for anticipation of population along a 3-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 cm s−2, the population is anticipated to experience growth of almost 30 % over the forecasted time span, which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area is anticipated to grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.

show abstract

Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima/Callao

Cited by 3 publications

References 29 publications

Between global risk reduction goals, scientific-technical capabilities and local realities: a novel modular approach for multi-risk assessment

Between global risk reduction goals, scientific-technical capabilities and local realities: a novel modular approach for multi-risk assessment

Anticipating a risky future: LSTM models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

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

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