Using Operational Earthquake Forecasting Tool for Decision Making: A Synthetic Case Study 

Abstract

<p>Operational earthquake forecasting (OEF) provides timely information about the time-dependent earthquake probabilities, which facilitates resilience-oriented decision-making. This study utilized the tools developed within the TURNkey (Towards more Earthquake-Resilient Urban Societies through a Multi-Sensor-Based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response Actions) project funded by the European Union’s Horizon 2020 research and innovation programme to demonstrate the benefits of OEF to the decision support system.  The considered tools are developed based on the state-of-the-art knowledge about seismology and earthquake engineering, involving the Bayesian spatiotemporal epidemic-type aftershock sequence (ETAS) forecasting model, the time-dependent probabilistic seismic hazard assessment, the SELENA (SEimic Loss EstimatioN using a logic tree Approach) risk analysis, cost-benefit analysis and the multi-criteria decision-making methodology. Moreover, the tools are connected to the dense seismograph network developed also within the TURNkey project and, thus, it is capable of real-time updating the forecasting based on the latest earthquake information and observations (e.g., earthquake catalogue). Through a case study in a synthetic city, this study first shows that the changes in the earthquake probabilities can be used as an indicator to inform the authorities or property owners about the heightened seismicity, based on which the decision-maker can, for example, issue a warning of the potential seismic hazard. Moreover, this study illustrates that OEF together with the risk and loss analysis provides the decision-maker with a better picture of the potential seismic impact on the physical vulnerabilities (e.g., damage, economic loss, functionality) and social vulnerabilities (e.g., casualty and required shelters). Finally, given the decision-maker’s preference, this study shows how the hazard and risk results are used to help the decision-maker to identify the optimal action based on cost-beneficial class and the optimality value computed based on the multi-criteria decision-making methodology.</p>