Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra

The nearly unprecedented loss of life resulting from the earthquake and tsunami of December 26, 2004, was greatest in the province of Aceh, Sumatra (Indonesia). We evaluated tsunami damage and built empirical vulnerability models of damage/no damage based on elevation, distance from shore, vegetation, and exposure. We found that highly predictive models are possible and that developed areas were far more likely to be damaged than forested zones. Modeling exercises such as this one, conducted in other vulnerable zones across the planet, would enable managers to create better warning and protection defenses, e.g., tree belts, against these destructive forces.

[1]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[2]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[3]  Vasily Titov,et al.  Implementation and testing of the Method of Splitting Tsunami (MOST) model , 1997 .

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[5]  R. Pumfrey,et al.  US agency for international development , 1997 .

[6]  Vasily Titov,et al.  Numerical Modeling of Tidal Wave Runup , 1998 .

[7]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[8]  J. Friedman.  Stochastic gradient boosting , 2002 .

[9]  E. Okal,et al.  Field Survey and Preliminary Modeling of the Wewak, Papua New Guinea Earthquake and Tsunami of 9 September 2002 , 2003 .

[10]  Alex Hagen,et al.  Fuzzy set approach to assessing similarity of categorical maps , 2003, Int. J. Geogr. Inf. Sci..

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  C. Synolakis,et al.  Tsunami sources in the southern California bight , 2004 .

[13]  Andy Liaw,et al.  New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis. , 2004 .

[14]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[15]  In Wake of Disaster, Scientists Seek Out Clues to Prevention , 2005, Science.

[16]  E. Kulikov,et al.  Estimation of Tsunami Risk for the Coasts of Peru and Northern Chile , 2005 .

[17]  M. K. Sørensen,et al.  The Asian Tsunami: A Protective Role for Coastal Vegetation , 2005, Science.

[18]  Greg Ridgeway,et al.  Generalized Boosted Models: A guide to the gbm package , 2006 .

[19]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[20]  Costas E Synolakis,et al.  Tsunami: wave of change. , 2006, Scientific American.

[21]  R. G. Pontlus.  Quantification Error Versus Location Error in Comparison of Categorical Maps , 2006 .