scholarly journals Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 55 ◽  
Author(s):  
Xuejun Zhu ◽  
Xiaona Jin ◽  
Dongdong Jia ◽  
Naiwei Sun ◽  
Pu Wang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Rock Burst ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Monitoring Data ◽  
Efficient Production ◽  
Mining Technology ◽  
Rock Bursts

In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real-time queries and the analysis of monitoring data through browsers, which is very convenient. Hence, it can make important contributions to the safe and efficient production of coal mines and the sustainable development of the coal economy.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal

2013 ◽  
pp. 1349-1383
Author(s):  
Hakikur Rahman
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Bay Of Bengal ◽  
Early Warning System ◽  
Ethical Issues ◽  
Warning System ◽  
Major Portion ◽  
Data Mining Techniques ◽  
Landing Zone ◽  
Bangladeshi Population

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.

scholarly journals Intelligent Early Warning System for Construction Safety of Excavations Adjacent to Existing Metro Tunnels

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tian ◽  
Jiang Meng ◽  
Xing-Ju Zhong ◽  
Xiao Tan
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Assessment Model ◽  
Monitoring Data ◽  
Effective Control ◽  
Construction Process ◽  
Safety Risk ◽  
Time Deformation ◽  
Metro Tunnel

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk-based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on-site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real-time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three-dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision-making.

Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal

2013 ◽  
pp. 53-86 ◽  
Author(s):  
Hakikur Rahman
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Bay Of Bengal ◽  
Early Warning System ◽  
Ethical Issues ◽  
Warning System ◽  
Major Portion ◽  
Data Mining Techniques ◽  
Landing Zone ◽  
Bangladeshi Population

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.

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
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Fuzzy Expert System ◽  
Warning Systems ◽  
Single Data ◽  
Set Up

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.

scholarly journals A Study about the 3S-based Great Ruins Monitoring and Early-warning System

2015 ◽  
Vol II-5/W3 ◽  
pp. 357-361
Author(s):  
W. Xuefeng ◽  
H. Zhongyuan ◽  
L. Gongli ◽  
Z. Li
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Processing System ◽  
Warning System ◽  
Monitoring Data ◽  
Software Systems ◽  
New Countryside ◽  
Monitoring Data Processing ◽  
Monitoring And Early Warning

Large-scale urbanization construction and new countryside construction, frequent natural disasters, and natural corrosion pose severe threat to the great ruins. It is not uncommon that the cultural relics are damaged and great ruins are occupied. Now the ruins monitoring mainly adopt general monitoring data processing system which can not effectively exert management, display, excavation analysis and data sharing of the relics monitoring data. Meanwhile those general software systems require layout of large number of devices or apparatuses, but they are applied to small-scope relics monitoring only. Therefore, this paper proposes a method to make use of the stereoscopic cartographic satellite technology to improve and supplement the great ruins monitoring index system and combine GIS and GPS to establish a highly automatic, real-time and intelligent great ruins monitoring and early-warning system in order to realize collection, processing, updating, spatial visualization, analysis, distribution and sharing of the monitoring data, and provide scientific and effective data for the relics protection, scientific planning, reasonable development and sustainable utilization.

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