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.

Early Warning System Framework Proposal, Based on Big Data Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 35-66
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic
Keyword(s):  
Big Data ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Data Environment ◽  
Set Up ◽  
Novel Concept ◽  
A Company

Early warning systems are made with the purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible. The Big Data environment gives new opportunities and new approaches in analytical processes. There are numerous ways how to set up early warning systems within a company. The Big Data environment forces companies to apply new ways of thinking and use new disposable data sources. This article gives a novel concept for an early warning system design within a company, which is applicable in different industries. The core of the proposed framework is a hybrid fuzzy expert system which can contain a variety of data mining predictive models responsible for some specific areas as addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within the rule blocks. As a part of this framework, SNA methods are also explained and introduced as powerful and unique tool to be used in modern early warning systems.

Development of early warning system for linear engineering - a case study on highway in Sichuan, China

2020 ◽  
Author(s):  
Ruihua Xiao
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Highway Safety ◽  
Warning Systems ◽  
Safety Control ◽  
Early Warning Model ◽  
Landslide Early Warning ◽  
Warning Model

<p>For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.</p>

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 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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