Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries

Fuzzy Systems ◽  
2017 ◽  
pp. 202-234
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.

Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.


Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic

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.


2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.


2010 ◽  
Vol 10 (11) ◽  
pp. 2215-2228 ◽  
Author(s):  
M. Angermann ◽  
M. Guenther ◽  
K. Wendlandt

Abstract. This article discusses aspects of communication architecture for early warning systems (EWS) in general and gives details of the specific communication architecture of an early warning system against tsunamis. While its sensors are the "eyes and ears" of a warning system and enable the system to sense physical effects, its communication links and terminals are its "nerves and mouth" which transport measurements and estimates within the system and eventually warnings towards the affected population. Designing the communication architecture of an EWS against tsunamis is particularly challenging. Its sensors are typically very heterogeneous and spread several thousand kilometers apart. They are often located in remote areas and belong to different organizations. Similarly, the geographic spread of the potentially affected population is wide. Moreover, a failure to deliver a warning has fatal consequences. Yet, the communication infrastructure is likely to be affected by the disaster itself. Based on an analysis of the criticality, vulnerability and availability of communication means, we describe the design and implementation of a communication system that employs both terrestrial and satellite communication links. We believe that many of the issues we encountered during our work in the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) on the design and implementation communication architecture are also relevant for other types of warning systems. With this article, we intend to share our insights and lessons learned.


2022 ◽  
pp. 195-216
Author(s):  
Dejan Vasović ◽  
Ratko Ristić ◽  
Muhamed Bajrić

The level of sustainability of a modern society is associated with the ability to manage unwanted stressors from the environment, regardless of origin. Torrential floods represent a hydrological hazard whose frequency and intensity have increased in recent years, mainly due to climate changes. In order to effectively manage the risks of torrents, it is necessary to apply early warning systems, since torrential floods are formed very quickly, especially on the watercourses of a small catchment area. The early warning system is part of a comprehensive torrential flood risk management system, seen as a technical entity for the collection, transformation, and rapid distribution of data. Modern early warning systems are the successors of rudimentary methods used in the past, and they are based on ICT and mobile applications developed in relation to the requirements of end users. The chapter presents an analysis of characteristic examples of the use. The main conclusion of the chapter indicates the need to implement early warning systems in national emergency management structures.


Author(s):  
Filiz Eryılmaz

International organizations as private sector institutions started to develop Early Warning System [EWS] models aiming to anticipate whether and when individual countries can collide with a financial crisis. EWS models can be made most useful to help sustain global growth and maintain financial stability, especially in light of the lessons learned from the current and past crises. This paper proposes Early Warning Systems (EWS) for Turkish Currency and Banking Crisis in 2000 and 2001. To that end “KLR model” or “signaling window” approach developed by Kaminski, Lorezondo and Reinhart (1998) is testified in the empirical part of this research and applied to a sample of Turkey macroeconomic data for the 1998-2003 monthly periods.


2020 ◽  
Author(s):  
Ruihua Xiao

<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>


Author(s):  
Hamidreza Mehri ◽  
Faeze Sepahi Zoeram ◽  
Fatemeh Majidpour ◽  
Zainab Anbari Nogyni ◽  
Reza Jafari Nodoushan

Background: Although early warning system processes follow precise models and scenarios, the human part is not fully understood. Most people before and during crises, act according to their interpretive plans, sometimes when the situation may not be dangerous, but can lead to dangerous reactions. The purpose of this study was to provide an indicator that can be used to assess people's understanding of early warning systems. Methods: This study is a descriptive-analytical study that was conducted in 2019 in a gas refinery in Iran. In the first step, the Perception Index questionnaire was translated into Persian with the help of English language experts. In the next step, the validity and reliability of the questionnaire were assessed. The questionnaires were distributed and completed among 168 refinery personnel. The collected data were analyzed using SPSS software version 24, and Pearson and Spearman correlation coefficients were determined by statistical tests. Results: The content validity index was 0.8, and the content validity ratio was 0.66. The general index of perception of the rapid warning system in this industry was 71.74 percent. Pearson correlation test did not show a significant correlation between age and perception index (r = 0.060), and also this test showed a positive correlation between perception index and work experience (r = 0.691). Spearman test was used to examine the relationship between two variables of education level and perception index. The results showed that there was a strong correlation between these two variables (rho = 0.746). Conclusion: The results showed that the perception index in this questionnaire has high validity and reliability and can be used in high-risk industries. The general perception index gained in this industry was in good condition, which means that people are more likely to be well aware at the time of an accident and will behave appropriately. However, it is suggested that the managers of the industry understudy hold training classes related to the early warning systems, hold emergency maneuvers, and familiarize the personnel with different scenarios.  


2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.


Author(s):  
Riyan Benny Sukmara ◽  
Ray Shyan Wu

Samarinda City is one of the most attractive cities in Borneo Island (Indonesia) and also as a capital city of EastBorneo Province. The expansion of urban areas becomes essential due to rapid population and housing demand. Base on the statistical report, the annual population growth rate is 0.018% from the year 2016-2017 with a total population of 843446 inhabitants. Many natural disasters occur in some areas in this city, especially flooding. This natural disaster occurs almost every year, many people suffered and forced to evacuate. In 2018 there is 3 flood event with 28311 people was suffered and evacuated, and 5170 houses were flooded [1]. During the flood event, it was very possible to gain damages to their property and make traffic stuck. One common way to reducing the damages is using Early Warning Systems (EWS). Early warning is a major element for disaster risk reduction, including damages. To prevent and mitigate the impact of a disaster, many countries had taken action to build various methods of a public warning system. An effective early warning system focused on people-centered and comprises the following element, such as risk knowledge, technical monitoring and service, communication and dissemination of warnings, and community response capability [2]. Related to the existing condition which Samarinda is a Muslim-dominated city and obviously has a lot of a number of mosques. This is a good potency to develop an early warning system because every mosque has a loudspeaker for echoing Adzan (Muslim prayer-calling). With this existing condition, the loudspeaker can be utilized as a flood outdoor-voice warning announcer. The aim of this study is to briefly introduce the strategy of dissemination early warning by utilizing mosques. The hope of early warning dissemination is giving enough time to the people to evacuate their property to reduce damages and possibly to giving information to avoiding traffic stuck (in a certain location)due to flooding. The results of this study can be used as input for decision-makers to develop effective flood management strategies and policies, especially in the case of an early warning system where not well-developed in Samarinda.


Sign in / Sign up

Export Citation Format

Share Document