Threats Complex Distributed Systems Parrying Based on their Development Prognostication

DOI

10.2991/aisr.k.201029.036How to use a DOI?

Keywords

Water level forecasting, flood situation, neural networks, neural network for forecasting

Abstract

This article proposes a method for countering threats in complex distributed systems based on predicting their development. Initially, the relevance of the topic under study is justified: it seems promising to use approaches that have found application in solving problems associated with the management of complex systems. Further, an artificial neural network is proposed for forecasting: its structure is shown, as well as a mathematical model of self-learning, which allows achieving more accurate (with less error) results in the framework of threat prediction (in this case, the level of water rise at gauging stations) in complex distributed systems. Testing was carried out, the purpose of which is to confirm the effectiveness of the proposed solution in the framework of forecasting threats in complex distributed systems: the error of the predicted values from real varies from 9% to 10%, which allows us to predict the flood situation in a few days.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Evgeny Palchevsky
AU  - Olga Khristodulo
AU  - Sergey Pavlov
PY  - 2020
DA  - 2020/11/10
TI  - Threats Complex Distributed Systems Parrying Based on their Development Prognostication
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 191
EP  - 194
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.036
DO  - 10.2991/aisr.k.201029.036
ID  - Palchevsky2020
ER  -