Using Cluster Analysis to Optimize Tsunami Evacuation Zones
William Power, Biljana Luković
GNS Science, Lower Hutt, New Zealand
GNS Science
New Zealand tsunami sources
Background Figures from: Integrated Tsunami Database for Pacific
Distant/Regional EarthquakesLocal Earthquakes
GNS Science
Tsunami warnings
• Divide the coast into zones
• Assign a threat level for each zone, based on maximum predicted water level
• Example is based on shipping forecast zones – not optimised for tsunami
Tsunami threat levels
Source: Mw 9.1 Southern Peru (1868)
GNS Science
Tsunami threat levels Tsunami threat levels
Source: Mw 9.1 Southern Peru (1868)
Source: Mw 9.1 Southern Peru (1868)
GNS Science
GNS Science
The Basic idea
Fault 1 Fault 2Max water level Fault 1
Max
wat
er le
vel F
ault
2
Cluster 1
Cluster 2
GNS Science
Source locations
GNS Science
GNS Science
Colours indicate clusters
GNS Science
GNS Science
GNS Science
Problem
• The standard algorithms for computing clusters do not require the members of the cluster to be contiguous
GNS Science
No relationship between separated clusters of the same colour
GNS Science
Conclusions
• New Zealand is exposed to tsunami from many directions
• Different parts of the coast are more/less susceptible to different source regions
• In a warning system based around zones it is beneficial if the coast within each zone has a similar pattern of susceptibility
• Cluster Analysis is one route for classifying stretches of coast according to their susceptibility to different sources
• A drawback of conventional cluster analysis is that it does not constrain the clusters to be contiguous around the coast
• Approaches to adding the contiguity constraint are possible, but more work is required
GNS Science
Acknowledgments
• NOAA – use of MOST and FACTS
• Diana Greenslade (BOM) – discussions about warning zones
• David Rhoades (GNS) – discussions about statistical analysis