Toward the integration of uncertainty and probabilities in spatial multi-criteria risk analysis

Abstract

Quantitative risk assessment supports decision-making processes in an increasing variety of contexts. Within the domain of environmental decision-making, the spatial distribution of impacts, vulnerabilities and consequences associated to different risk-mitigation alternatives calls for a different framework, i.e. spatial multi-criteria risk analysis. In this paper, we propose the combined use of a hierarchical Bayesian modelling approach and Geographic Information Systems to integrate uncertainty and model probabilities into an overall risk map. The research aims at testing the operability of the integration between Bayesian modelling and spatial analysis in the context of spatial risk processes for resource allocation. To this end, we applied the model on a case study dealing with tanker oil spill risk in the Mediterranean Sea. The innovative contribution of the study stems from both the context of application point of view and the use of Bayesian modelling to calculate not only probabilities but an overall spatial risk measure.

Energy-related Severe Accident Database (ENSAD): cloud-based geospatial platform

“…The range of potential applications for this kind of tools in ENSAD v2.0 is large, and it includes all energy chains and infrastructure types as well as different types of accident consequences. For example, effects of refinery configuration and geographic location on outcome risk levels or visualisation of spatial risk assessment of oil spills (Spada and Ferretti, 2018) are two cases currently considered for implementation.…”

Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database

“…Here, we explore the chains-of-events leading to severe accidents in the oil & gas industry using a data-driven approach based on ENSAD, which has been described as the most authoritative resource for comparative risk analysis of accidents in the energy sector [ 11 , 13 , 19 ]. This is the first study to apply graph theory and catastrophe dynamics modelling [ 25 – 28 ] to ENSAD, allowing us to describe the general topological properties of severe accidents at refineries [ 29 ], oil tankers [ 30 ] and gas networks [ 31 ], based on a rich database of more than a thousand events, spanning from 1970 to 2016, that includes information on the chains-of-events that led to these accidents. By describing severe accidents at critical infrastructures (CIs) with dependency risk graphs, we can first identify their main sources, catalysts, and sinks.…”

“…However, the natural gas network includes the entire energy chain, from exploration and production, storage and distribution, and power plants. While the distribution of accidents is worldwide for refineries and gas networks, for tanker accidents, only those in the Mediterranean Sea were considered (one of the most active and accident-prone area in the world) [ 30 ].…”

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

“…Hence, there is a clear need for more and better data, and accordingly, improved uncertainty quantification for accident risk indicators, because they are the basis to support decision-makers and risk managers in their efforts to design and implement better risk management strategies, risk mitigation and prevention measures processes [10][11][12]. More recently, resilience-driven strategies have been proposed for environmental systems (e.g., marine ecosystems) that are synergistically based on risk assessment to appropriately protect against uncertain and unexpected events such as, for example, oil spills [13]. In the past years, numerous overviews and summaries about conceptual and methodologial treatments of uncertainty within risk assessment have been published [14][15][16][17][18][19].…”

“…In particular, in [20], credibility intervals (e.g., 5-95%) were firstly constructed for point estimates using a Chi-square distribution [21]; however, without considering that the uncertainty is a combination, among others, of epistemic and aleatory factors [22]. In order to consider a more complex uncertainty assessment, [13,[23][24][25] analysed the risk of accidents in different energy chains chains using a Bayesian inference. In this way, the authors could estimate the risk indicators and their uncertainties, since the latter is intrinsically assessed through a Bayesian approach.…”