Accounting for end-user preferences in earthquake early warning systems
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Earthquake early warning systems (EEWSs) that rapidly trigger risk-reduction actions after a potentially-damaging earthquake is detected are an attractive tool to reduce seismic losses. One brake on their implementation in practice is the difficulty in setting the threshold required to trigger pre-defined actions: set the level too high and the action is not triggered before potentially-damaging shaking occurs and set the level too low and the action is triggered too readily. Balancing these conflicting requirements of an EEWS requires a consideration of the preferences of its potential end users. In this article a framework to define these preferences, as part of a participatory decision making procedure, is presented. An aspect of this framework is illustrated for a hypothetical toll bridge in a seismically-active region, where the bridge owners wish to balance the risk to people crossing the bridge with the loss of toll revenue and additional travel costs in case of bridge closure. Multi-attribute utility theory (MAUT) is used to constrain the trigger threshold for four owners with different preferences. We find that MAUT is an appealing and transparent way of aiding the potentially controversial decision of what level of risk to accept in EEW.
KeywordsEarthquake early warning (EEW) Decision making End-user preferences Bridges Thresholds Multi-attribute utility theory (MAUT)
This study was supported by REAKT (Strategies and tools for Real Time EArthquake RisK ReducTion), a Framework 7 project funded by the European Commission (ENV.2011.1.3.1-1). We thank Gordon Woo for discussions on decision making. We thank two anonymous reviewers for their extensive and detailed comments on an earlier version of this article.
- Douglas J, Woo G, Auclair S, Le Guenan T (2012), Critical review of participatory decision making in fields other than earthquake risk reduction. Report BRGM/RP-61480-FR. http://www.brgm.eu/content/public-reports
- FEMA (2003) Multi-hazard loss estimation earthquake model HAZUS-MH MR3 technical manualGoogle Scholar
- Giardini D, Woessner J, Danciu L, Crowley H, Cotton F, Grünthal G, Pinho R, Valensise G, Akkar S, Arvidsson R, Basili R, Cameelbeeck T, Campos-Costa A, Douglas J, Demircioglu MB, Erdik M, Fonseca J, Glavatovic B, Lindholm C, Makropoulos K, Meletti F, Musson R, Pitilakis K, Sesetyan K, Stromeyer D, Stucchi M, Rovida A (2013) Seismic hazard harmonization in Europe (SHARE): Online Data Resource. doi: 10.12686/SED-00000001-SHARE
- Le Guenan T, Smai F, Loschetter A, Auclair S, Douglas J (2014) Proposed participatory decision-making framework of REAKT and application to test cases. Deliverable D6.7, REAKT (Strategies and tools for Real Time EArthquake RisK ReducTion). http://www.reaktproject.eu/deliverables/REAKT-D6.7.pdf
- Nakamura Y, Saita J (2007) UrEDAS, the earthquake warning system: today and tomorrow. In: Gasparini P, Manfredi G, Zschau J (eds) Earthquake early warning systems. Springer, Berlin, pp. 249–281Google Scholar
- Neapolitan RE (2004) Learning bayesian networks, vol 38. Prentice Hall, Upper Saddle RiverGoogle Scholar
- Taillefer N, Monfort-Climent D, Bastone V (2014) Loss estimation for co-seismic risk assessment through an EWS. Report BRGM/RP-63614-FR. http://www.brgm.eu/content/public-reports
- Von Neumann J, Morgenstern O (1953) Theory of games and economic behavior. Princeton University Press, PrincetonGoogle Scholar