SUMMARYWe study the seismic vulnerability of the interdependent European gas and electricity transmission networks from a topological point of view, whereby the electricity network depends on the gas network through gas-fired power plants. First, we assessed the seismic response for each independent network; then we analyzed the increased vulnerability due to their interdependency. We implemented a probabilistic reliability model that encompasses the spatial distribution of both network structures and their seismic hazard exposure using a Geographic Information System. We characterized the network interdependency using the strength of coupling of the interconnections, together with the seismic response of the independent-gas-network. We calculated the network fragility curves of the independent and dependent networks in terms of various performance measures (connectivity loss, power loss, and impact on the population) and found that the gas network is more seismically vulnerable than the electricity network. The interdependency introduces an extra vulnerability to the electricity network response that decreases with the extensiveness of the networks' damage states. Damage was also evaluated at a local level in order to identify the most vulnerable parts of the network, where it was found that the potential for the highest power loss is located in southeast Europe.

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Here, we uncover the load and fault-tolerant backbones of the trans-European gas pipeline network. Combining topological data with information on intercountry flows, we estimate the global load of the network and its tolerance to failures. To do this, we apply two complementary methods generalized from the betweenness centrality and the maximum flow. We find that the gas pipeline network has grown to satisfy a dual purpose. On one hand, the major pipelines are crossed by a large number of shortest paths thereby increasing the efficiency of the network; on the other hand, a nonoperational pipeline causes only a minimal impact on network capacity, implying that the network is error tolerant. These findings suggest that the trans-European gas pipeline network is robust, i.e., error tolerant to failures of high load links.

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Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at ‘Punta della Salute’ from Venice Lagoon during the years 1980–1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the ‘normal’ dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the ‘high water’ phenomenon more than 2–3 hours ahead.