scholarly journals The Concept of Human Security as a Basis for the Application of Big Data Concept in Establishment of Early Warning System for Crisis Management in the Republic of Croatia

2020 ◽  
Vol 26 (86) ◽  
pp. 72-95
Author(s):  
Ivana Cesarec ◽  
Robert Mikac ◽  
Davor Spevec
Keyword(s):  
Big Data ◽  
Crisis Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Human Security ◽  
Crisis Events ◽  
Response Systems ◽  
The Republic ◽  
Globalized World

We live in a globalized world characterized by constant crises in numerous social and geographical areas. Political instability, climate change, overpopulation, uncontrolled migration, poor governance, crime, as well as many other factors create the circumstances from which crises can develop. Each crisis given its causes and possible consequences requires different approaches and response systems. This research focuses on considering modern technological solutions that have the purpose of alerting and protecting individuals from risks and threats that can lead to their suffering, caused by natural, technical-technological and anthropogenic crisis events. It also aims to link the theory of human security and the big data concept and present their application through the development of the early warning system for crisis management in the Republic of Croatia. This research has significant value because it analyses and describes the establishment of a particular system in the world.

scholarly journals SARS-CoV-2 crisis management with a wastewater early-warning system in the Bavarian district of Berchtesgadener Land, Germany

2021 ◽  
Author(s):  
Katalyn Rossmann ◽  
Rüttger Clasen ◽  
Manuel Münch ◽  
Christian Wurzbacher ◽  
Andreas Tiehm ◽  
...  
Keyword(s):  
Crisis Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

scholarly journals RESEARCH ON EARLY WARNING SYSTEM OF COLLEGE STUDENTS’ BEHAVIOR BASED ON BIG DATA ENVIRONMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 659-665
Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang
Keyword(s):  
College Students ◽  
Big Data ◽  
Early Warning ◽  
Behavioral Problems ◽  
Early Warning System ◽  
Warning System ◽  
Safety Control ◽  
Education Management ◽  
Data Environment ◽  
Behavior Based

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.

Design of transmission line safety early warning system based on big data variable analysis

2021 ◽  
Author(s):  
Guangxin Zhang ◽  
Liying Zhao ◽  
Dongliang Qiao ◽  
Ziwen Shang ◽  
Rui Huang
Keyword(s):  
Big Data ◽  
Transmission Line ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Variable Analysis

scholarly journals A Semantic IoT Early Warning System for Natural Environment Crisis Management

2015 ◽  
Vol 3 (2) ◽  
pp. 246-257 ◽  
Author(s):  
Stefan Poslad ◽  
Stuart E. Middleton ◽  
Fernando Chaves ◽  
Ran Tao ◽  
Ocal Necmioglu ◽  
...  
Keyword(s):  
Crisis Management ◽  
Early Warning ◽  
Natural Environment ◽  
Early Warning System ◽  
Warning System

scholarly journals Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

2017 ◽  
Vol 17 (10) ◽  
pp. 1713-1723 ◽  
Author(s):  
Emanuele Intrieri ◽  
Federica Bardi ◽  
Riccardo Fanti ◽  
Giovanni Gigli ◽  
Francesco Fidolini ◽  
...  
Keyword(s):  
Big Data ◽  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Early Warning Systems ◽  
Integrated System ◽  
Monitoring Systems ◽  
Landslide Risk ◽  
Complex Data

Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

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