Towards a responsible early-warning system: Knowledge implications in decision support design

Arru, Maude; Negre, Elsa; Rosenthal‐Sabroux, Camille; Grundstein, Michel

doi:10.1109/rcis.2016.7549288

Cited by 2 publications

(1 citation statement)

References 23 publications

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“… Experience: crisis faced in the past, objective ability to escape/defend, subjective ability to escape/defend.  Explicited knowledge: general knowledge shared by the population, training followed, access to documents, access to knowledge sharing tools [19].  Risk assessment: objective assessment, subjective assessment.…”

Section: Behavioral Factors Studiedmentioning

confidence: 99%

Population Behaviors in Crisis Situations - A Study of Behavioral Factors in the PPI Ineos Emergency Response Exercise

Arru¹,

Negre²,

Rosenthal‐Sabroux³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Warnings can help to prevent damage and harm if they are issued timely and provide information that helps respondents and population to adequately prepare for the disaster to come. Today, many indicators and sensor systems are designed to produce alert and reduce disaster risks. These systems have proved to be effective but, as all systems including human beings, part of the system remains unpredictable. Each person behaves differently when a problem arises. We study in this paper the reactions of the population of Verdun, in France, during a public safety exercise. This exercise simulated a chemical risk alert, including the population participation. We propose here an analysis of people's reactions during this exercise, based on interviews and surveys, and according to different behavioral factors.

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Section: Behavioral Factors Studiedmentioning

confidence: 99%

Population Behaviors in Crisis Situations - A Study of Behavioral Factors in the PPI Ineos Emergency Response Exercise

Arru¹,

Negre²,

Rosenthal‐Sabroux³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

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Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System

Jia

Wang

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The rapid development of computers has improved the scope of dynamic knowledge network learning applications. Online learning has brought convenience to people in time and place. At the same time, people began to pay attention to the efficiency and quality of online learning. At present, the network knowledge storage system is distributed storage system. The distributed storage system has great performance in terms of capacity, scalability, and parallelism. However, its storage node is inexpensive, and the reliability is not high, and it is prone to fault. Based on the designed fault detection model detection path, relying on building the knowledge data node fault detection mode, constructing the knowledge data link fault detection mode, completing the fault detection model detection mode, and finally realizing the dynamic knowledge network learning system fault intelligent detection technology research. The experiment proves that the dynamic knowledge network learning system fault intelligent detection technology designed in this paper reduces the fault rate of the network learning system by 37.5%.

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Towards a responsible early-warning system: Knowledge implications in decision support design

Cited by 2 publications

References 23 publications

Population Behaviors in Crisis Situations - A Study of Behavioral Factors in the PPI Ineos Emergency Response Exercise

Population Behaviors in Crisis Situations - A Study of Behavioral Factors in the PPI Ineos Emergency Response Exercise

Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System

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