UDK 004.9 Doi: 10.31772/2587-6066-2020-21-3-323-332
EFFICIENCY IMPROVING OF EMERGENCY MONITORING AND FORECASTING BASED ON THE INFORMATION SYSTEM
I. N. Pozharkova
Siberian Fire and Rescue Academy EMERCOM of Russia; 1, Severnaya St., Zheleznogorsk, 662972, Russian Federation
The article is devoted to the automated information system modification to solve monitoring and forecasting problems of natural and man-made emergencies in order to increase the efficiency of its functioning, namely, to increase the execution speed of the main operations, to reduce the error probability. Monitoring and forecasting of emergencies are among the priorities in the field of population from emergencies protection, as the prevention and elimination of their consequences are carried out on the basis of these tasks. At the same time, the data collection speed, processing and analysis largely determine the efficiency of the obtained results. The existing system of monitoring and forecasting of natural and man-made emergencies, its functional model in IDEF0 notation, characteristic features, advantages and disadvantages are considered. The existing system can be improved by automating a number of tasks related to the processing, transmission and storage of large data amounts, including real time data, as well as the generation of consolidated reports on the results of monitoring and forecasting of various objects. The information architecture of the solution reviewed and the corresponding database model form the basis of the proposed solution. The IDEF0 model of emergency monitoring and forecasting has been introduced taking into account the proposed modification of the automated information system. The main operation execution time comparative analysis based on the initial and modified automated information system (AIS) using the existing hardware confirms the effectiveness of the proposed solution. Data exchange and generation automation of consolidated reports on multiple monitoring objects will simplify analysis of the obtained results and solutions development based on them aimed at prevention of natural and man-made emergencies, as well as elimination of their consequences.
Keywords: automated information system (AIS), emergency monitoring and forecasting, automation, data conversion.
References

1. Order No. 94 of the Ministry of Civil Defence,
Emergency Situations and Natural Disaster Response
of 4 March 2011 approving the Regulations on the Functional
Subsystem for Monitoring, Laboratory Control and
Forecasting of Emergencies of the Unified State Emergency
Management System.
2. Pozharkova I. N., Ziablicki A. M. Analysis of the
system of monitoring and forecasting of emergency situations
of the Republic of Altai as an object of automation.
Collection of materials of the All-Russian scientific and
practical conference “Monitoring, modeling and forecasting
of natural hazards and emergencies”. Zheleznogorsk,
2019. P. 156–160.
3. Systems engineering fundamentals. Available at:
https://ocw.mit.edu/courses/aeronautics-and-astronautics/
16-885j-aircraft-systems-engineering-fall-2005/readings/
sefguide_01_01.pdf (accessed: 14.1.2020).
4. Mineharu S., Hiroko N. Raw-to-repository characterization
data conversion for repeatable, replicable, and
reproducible measurements. Journal of Vacuum Science
& Technology. 2019, Vol. 12, P. 125–144.
5. Lei Zh., Li-Gang S. Research on data preprocess in
data mining. Computer science and engineering. 2018,
Vol. 7, P. 314–328.
6. Marz N., Warren D. Big Data. Principles and practices
for building scalable real-time data processing
systems. Moscow, Williams Publ., 2018, 582 p.
7. Malik K., Farhan M. Big-data: transformation from
heterogeneous data to semantically-enriched simplified
data. Multimedia Tools and Applications. 2016, Vol. 75,
P. 12727–12747.
8. Yong-Min L., Won-Bog L. The Development of
Protocol for Construction of Smart Factory. Journal
of IKEEE. 2019, Vol. 23, P. 1096–1099.
9. Perry D., Parsons N., Costa M. ‘Big data’ reporting
guidelines how to answer big questions, yet avoid big
problems. The Bone & Joint Journal. 2014, Vol. 96-B,
P. 7–32.
10. Taleb I., Dssouli R. Big Data Pre-processing:
A Quality Framework. IEEE International Congress on
Big Data. 2015, P. 737–751.
11. Alberti-Alhtaybat L. Big Data and corporate reporting:
impacts and paradoxes. Accounting, Auditing &
Accountability Journal. 2015, Vol. 5, P. 85–102.
12. Henningsson S., Yetton P., Wynne P. A review
of information system integration in mergers and acquisi
tions. Journal of Information Technology. 2018, Vol. 33,
P. 255–303.
13. Dughmi S. Algorithmic information structure
design. ACM SIGecom Exchanges. 2017, Vol. 2,
P. 244–252.
14. Teorey T., Lightstone S., Nadeau T. Database
Modeling and Design: Logical Design. Morgan Kaufmann,
2011. 333 p.
15. Resolution of the Government of the Russian
Federation No. 334 24 March 1997, 2017 On the Procedure
for the Collection and Exchange in the Russian
Federation of Information in the Field of Protection
of the Population and Territories from Natural and Manmade
Emergencies.


Pozharkova Irina Nikolaevna – Cand. Sc., the associate professor, professor of department of technical examinations
and criminalistics; Siberian Fire and Rescue Academy EMERCOM of Russia. E-mail: pozharkova@mail.ru.


  EFFICIENCY IMPROVING OF EMERGENCY MONITORING AND FORECASTING BASED ON THE INFORMATION SYSTEM