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/).
Cite this article
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 -