This paper presents a framework and data for spatially distributed assessment of tsunami inundation models. Our associated validation test is based upon the 2004 Indian Ocean tsunami, which affords a uniquely large amount of observational data for events of this kind. Specifically, we use eyewitness accounts to assess onshore flow depths and speeds as well as a detailed inundation survey of Patong City, Thailand to compare modelled and observed inundation. Model predictions matched well the detailed inundation survey as well as altimetry data from the JASON satellite, eyewitness accounts of wave front arrival times and onshore flow speeds. Important buildings and other structures were incorporated into the underlying elevation model and are shown to have a large influence on inundation extent.
We present a parallel 2D wavelet transform algorithm with modest communication requirements. Data are transmitted between nearest neighbors only and the amount is independent of the problem size as well as the number of processors. An analysis of the theoretical performance shows that the algorithm is scalable approaching perfect speedup as the problem size is increased. This performance is realized in practice on the IBM SP2 as well as on the Fujitsu VPP300 where it will form part of the Scientific Software Library.
We introduce in this paper a new predictive modelling method to analyse geographic data known as sparse grids. The sparse grids method has been developed for data-mining applications. It is a machine-learning approach to data analysis and has great applicability to the analysis and understanding of geographic data and processes. Sparse grids are a subset of grid-based predictive modelling approaches. The advantages they have over other grid-based methods are that they use fewer parameters and are less susceptible to the curse of dimensionality. These mean that they can be applied to many geographic problems and are readily adapted to the analysis of geographically local samples. We demonstrate the utility of the sparse grids system using a large and spatially extensive data set of regolith samples from Weipa, Australia. We apply both global and local analyses to find relationships between the regolith data and a set of geomorphometric, hydrologic and spectral variables. The results of the global analyses are much better than those generated using an artificial neural network, and the local analysis results are better than those generated using moving window regression for the same analysis window size. The sparse grids system provides a potentially powerful tool for the analysis and understanding of geographic processes and relationships.
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