HR: 10:50h
AN: S22B-02 [Abstracts]
TI: Earthquake Prediction and Disaster Preparedness: Interactive Algorithms
AU: * Keilis-Borok, V
EM: vkb@ess.ucla.edu
AF: Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA 90095
United States
AU: * Keilis-Borok, V
EM: vkb@ess.ucla.edu
AF: International Institute of Earthquake prediction Theory and Mathematical Geophysics, Warshavskoe sh.,
79, korp. 2, Moscow, 113556
Russian Federation
AU: Davis, C
EM: Craig.Davis@LADWP.com
AF: Department of Water and Power, 111 N. Hope st., Los Angeles, CA 90051
United States
AU: Molchan, G
EM: molchan@mitp.ru
AF: International Institute of Earthquake prediction Theory and Mathematical Geophysics, Warshavskoe sh.,
79, korp. 2, Moscow, 113556
Russian Federation
AU: Molchan, G
EM: molchan@mitp.ru
AF: International Center for Theoretical Physics, Strada Costiera 11, Trieste, 34100
Italy
AU: Shebalin, P
EM: shebalin@mitp.ru
AF: International Institute of Earthquake prediction Theory and Mathematical Geophysics, Warshavskoe sh.,
79, korp. 2, Moscow, 113556
Russian Federation
AU: Shebalin, P
EM: shebalin@mitp.ru
AF: Institute de Physique du Globe, 4, Place Jussieu, Paris, 75005
France
AU: Lahr, P
EM: Philip.Lahr@LADWP.com
AF: Department of Water and Power, 111 N. Hope st., Los Angeles, CA 90051
United States
AU: Plumb, C
EM: Cliff.Plumb@water.ladwp.com
AF: Department of Water and Power, 111 N. Hope st., Los Angeles, CA 90051
United States
AB:
Recent studies in prediction of destructive earthquakes months in advance lead to a reformulation of the interwined problems
linking disaster disaster preparedness with earthquake prediction. Given a specific earthquake prediction including a time
window, magnitude and geographic area, the disaster manager has to choose the optimal set of temporal preparedness measures,
taking into account its level of uncertainty. The "predictor" has to ensure this optimization by setting up an appropriate
tradeoff between different kinds of prediction errors. Both problems belong to the broad field of decision-making based on
incomplete information. We hypothetically explore these problems using a simplified example, based on a small portion of a
major water utility, and present concepts that can be practically and effectively utilized for decision support by disaster
managers.
DE: 7200 SEISMOLOGY
SC: Seismology [S]
MN: 2004 AGU Fall Meeting