A Parallel Flood Inundation Algorithm for Massive DEM Data

The traditional flood inundation algorithms generally adopt seed filling algorithm along with several improved serial methods to evaluate flooding region. When dealing with huge amount of data, some of these algorithms fail due to large recursive depth, others end up spending overmuch time in obtaining large flooding zone. Due to the dependency of digital terrain data in flooding analysis, few parallel flooding algorithms has been developed to improve the computational efficiency, which fails to make full use of the performance advantages of computer clusters. To solve above problems, this paper proposes a parallel flood inundation algorithm for massive DEM data. The algorithm presents a boundary component method using strip data partition and run length code to generate potential flooding area in parallel, after which it traverses all strips in sequence to gain whole flooding region. The computational efficiency of the proposed algorithm is verified by comparing it with the strip seed filling algorithm over practical DEM data. Meanwhile, the capability to process massive data of the proposed algorithm is proved by experimental results.