A Data Exchange Tool Based on Ontology for Emergency Response Systems

Simas, Félix; Barros, Rebeca; Salvador, Laís do Nascimento; Weber, Marian; Amorim, Simone da Silva

doi:10.1007/978-3-319-70863-8_7

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…High-resolution nowcasting for extreme weather is an essential tool needed for effective adaptation to climate change and in particular deep learning techniques have shown dramatic promise in high-resolution (1 km × 1 km) short-term (1 h) predictions of precipitation [Agrawal S., Hicdkey J., Xingjian S. et al 2019]. Lastly, to be able to model a trigger of geospatially localized natural adverse events, is also useful to implement an ontology interoperable disaster risk reduction system (DRR) and to organize an Emergency Response System (ERS) [ 1 , 8 , 16 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Models Applied to Weather Series Analysis

Fallucchi

Scano

Luca

2021

Metadata and Semantic Research

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In recent years the explosion in high-performance computing systems and high-capacity storage has led to an exponential increase in the amount of information, generating the phenomenon of big data and the development of automatic processing models like machine learning analysis. In this paper a machine learning time series analysis was experimentally developed in relation to the paroxysmal meteorological event “cloudburst” characterized by a very intense storm, concentrated in a few hours and highly localized. These extreme phenomena such as hail, overflows and sudden floods are found in both urban and rural areas. The predictability over time of these phenomena is very short and depends on the event considered, therefore it is useful to add data driven methods to the deterministic modeling tools to get the anticipated predictability of the event, also known as nowcasting. The detailed knowledge of these phenomena, together with the development of simulation models for the propagation of cloudbursts, can be a useful tool for monitoring and mitigating risk in civil protection contingency plans.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Models Applied to Weather Series Analysis

Fallucchi

Scano

Luca

2021

Metadata and Semantic Research

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“…In the following, we shortly describe each vocabulary. U F D MC #C #R IV HXL Disaster,Geography,Damage, TTL [3], [1], [4], [5] 50 66 [6], [7], [8] Organization, Humanitarian Response [9], [10], [11], [12], [13] MOAC Impact of Crisis, Recovery RDF [3], [14] 70 30 [7], [15] and Response Activities, [10], [16] Geo-locations [17], [18] SMEM Social Media and Emergency --- [19], [20], [21], [22], [7], [8] Management [23], [24], [1], [25], [26], [27] DO Temporal and Spatial Web--97 --Concepts, Impact, App Rehabilitation and Facilities Facilities ERO Report Specification [28], [29], [30], [31], [32] -- [33], [34], [ [37], [19], [20], [8], [7] Crisis Management, Hazard Events…”

Section: State-of-the-art Vocabulariesmentioning

confidence: 99%

Empathi: An Ontology for Emergency Managing and Planning About Hazard Crisis

Shekarpour

Gyrard²,

Sheth³

2019

2019 IEEE 13th International Conference on Semantic Computing (ICSC)

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In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/.

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Research on Heterogeneous Data Exchange Technology Based on Shadow Table

Zhang

Yang

et al. 2019

Recent Trends in Intelligent Computing, Communication and Devices

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A Data Exchange Tool Based on Ontology for Emergency Response Systems

Cited by 4 publications

References 3 publications

Machine Learning Models Applied to Weather Series Analysis

Machine Learning Models Applied to Weather Series Analysis

Empathi: An Ontology for Emergency Managing and Planning About Hazard Crisis

Research on Heterogeneous Data Exchange Technology Based on Shadow Table

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