Tsunami-Related Data: A Review of Available Repositories Used in Scientific Literature

Nacházel, Tomáš; Babič, František; Baiguera, Marco; Čech, Pavel; Husáková, Martina; Mikulecký, Peter; Mls, Karel; Ponce, Daniela; Salmanidou, Dimitra; Štekerová, Kamila; Triantafyllou, Ioanna; Tučník, Petr; Zanker, Marek; Bureš, Vladimír

doi:10.3390/w13162177

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…As such, agent-based (social) simulations have the potential to contribute meaningfully to a better understanding of the large-scale processes related to evacuation procedures and the mitigation of risks related to disasters. This review is also related to our previous research (Nacházel et al 2021 ) focused on analyzing tsunami-related data and datasets. At the same time, the possibilities of ontologies as perspective methods of representation and formal description of the observed domain are investigated (Babič et al 2022 ).…”

Section: Introductionmentioning

confidence: 90%

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

et al. 2022

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This systematic review provides a comprehensive overview of tsunami evacuation models. The review covers scientific studies from the last decade (2012–2021) and is explicitly focused on models using an agent-based approach. The PRISMA methodology was used to analyze 171 selected papers, resulting in over 53 studies included in the detailed full-text analysis. This review is divided into two main parts: (1) a descriptive analysis of the presented models (focused on the modeling tools, validation, and software platform used, etc.), and (2) model analysis (e.g., model purpose, types of agents, input and output data, and modeled area). Special attention was given to the features of these models specifically associated with an agent-based approach. The results lead to the conclusion that the research domain of agent-based tsunami evacuation models is quite narrow and specialized, with a high degree of variability in the model attributes and properties. At the same time, the application of agent-specific methodologies, protocols, organizational paradigms, or standards is sparse. Supplementary Information The online version contains supplementary material available at 10.1007/s11069-022-05643-x.

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Section: Introductionmentioning

confidence: 90%

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

et al. 2022

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“…Data ecosystems collect and manage many data types from different sources to produce new knowledge (Cui et al , 2020; Ramalli et al , 2023). In the past years, scientific repositories and data ecosystems have been increasing in different fields (Blaiszik et al , 2019; Nacházel et al , 2021; Ramalli et al , 2021b). Their data management capabilities and services define what can be discovered (Allan et al , 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge graph embedding for experimental uncertainty estimation

Ramalli

Pernici²

2023

IDD

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Purpose Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments. Design/methodology/approach This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study. Findings The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata. Originality/value The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

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“…Studies have attempted to clarify the characteristics and mechanisms of tsunami-induced damage and develop effective countermeasures against future tsunamis. According to Nacházel et al [3], the number of these studies rapidly increased after the 2004 Indian Ocean tsunami, 2011 Tohoku earthquake tsunami, and 2018 Sulawesi tsunami.…”

Section: Introductionmentioning

confidence: 99%

Mechanism of Tsunami-Induced Erosion of Bridge-Abutment Backfill and Its Countermeasures

Nakamura

Sugiyama

Cho

et al. 2021

Water

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Tsunamis can destroy bridges in coastal areas. Studies have attempted to unravel the mechanism of tsunami-induced damage and develop effective countermeasures against future tsunamis. However, the mechanisms of tsunami-induced erosion of bridge-abutment backfill and its countermeasures have not been studied adequately. This study investigates this topic using numerical analysis. The results show that the tsunami flowing down along the downstream wing of the abutment induces bedload sediment transport on the ogive section of the backfill on the downstream side of the abutment, resulting in the onset of backfill erosion. Sediment suspension and bedload sediment transportation occur when the backfill inside the abutment starts to flow out from below the downstream wing. This leads to subsidence of the backfill at the upstream side of the downstream wing. The subsequent backfill erosion is mainly caused by bedload sediment transport. Numerical experiments on countermeasures show that extending the wings downward can prevent the acceleration of backfill erosion in the presence of the abutment. A combination of multiple countermeasures, including extended wings, would be more effective in maintaining the stability of the abutment after a tsunami. This suggests the application of such countermeasures to actual bridges as an effective countermeasure against backfill erosion.

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Tsunami-Related Data: A Review of Available Repositories Used in Scientific Literature

Cited by 8 publications

References 47 publications

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

Knowledge graph embedding for experimental uncertainty estimation

Mechanism of Tsunami-Induced Erosion of Bridge-Abutment Backfill and Its Countermeasures

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