RiskNet: neural risk assessment in networks of unreliable resources

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology (P.B., P.C.) and by the PL-Grid Infrastructure (K.R.).Peer ReviewedPostprint (published version

ADVANCED RISK MANAGEMENT OF AN ARCTIC MARINE SEISMIC SURVEY OPERATION

This research is motivated by the lack of a robust risk management framework addressing the high risks in Arctic Marine Seismic Survey Operations (AMSSO), and the lack of transparent decision-making in Arctic shipping risk management globally. The literature review carried out herein reveals that the AMSSO and Arctic navigation involve significant risks caused by human elements and the unique features of this region. These known risk factors combine to constitute a ship-ice collision risk. This last represents the goal of the research investigation. With the complexity of the AMSSO system, three technical chapters are proposed to analyse and reduce the risks in the AMSSO. The first technical chapter deals with local risk analysis of the system. Herein, a Fuzzy Rule-based methodology is developed employing the probability distribution assessment in the form of belief degrees with Bayesian Network (BN) and Failure Mode and Effect Analysis (FMEA) for estimating the risk parameters of each hazard event using a computer-aided analysis. A case study of the application of the proposed risk model – Fuzzy Rule-based Bayesian Network (FRBN) –, in the Greenland, Iceland and Norwegian Seas (GNIS) AMSSO is carried out to identify the most critical hazard event in the prospect oil field. The second technical chapter deals with the global safety performance of the Ship-Ice Collision model dovetailing the Evidential Reasoning (ER) technique and Analytic Hierarchy Process (AHP) with the FRBN. A trial application of the global safety performance of the Ship-Ice Collision case in a prospect oil field is carried out to determine the safety level of AMSSO, measured against a developed benchmark risk. The outcome of the investigation reveals the Risk Influence Factor (RIF) of each hazard event in AMSSO. Since the risk level is far above the tolerable region of the developed benchmark risk, several Risk Control Options (RCOs) are investigated in the last technical chapter to reduce and control the critical risks. This technical chapter finalises the risk management framework developed in this research. In a trial application of reducing a critical risk in AMSSO, AHP-TOPSIS is utilised to find a balance between cost and benefit in selecting the most appropriate RCO at the heart of several RCOs and their associated criteria. The novelty of this research lies in the fact that it tackles the major concerns in risk analysis (concerns such as dynamic event risk analysis, hazard data uncertainties, and hazard event dependencies) of a complex system. More also, it adopts a hybrid methodology that offers a non-monotonic utility output to select the most appropriate RCO amongst several RCOs and conflicting criteria, to reduce the critical risks in AMSSO, in an economically viable strategy