Abstract
Many times in history, river floods have resulted in huge catastrophes. To reduce the negative outcome of such floods, it is important to predict their extent before they happen. For this reason, specialists use algorithms that model river floods on digital terrains datasets. Nowadays, massive terrain datasets have become widely available. As flood modeling is an important part for a wide range of applications, it is crucial to process such datasets fast even with standard computers. Yet, these datasets can be several times larger than the main memory of a standard computer. Unfortunately, existing flood-modeling algorithms cannot handle this situation efficiently. Hence they have to sacrifice output quality for time performance, or vice versa.
In this paper, we present a novel algorithm that, unlike any previous approach, can both provide high-quality river flood modeling and handle massive terrain data efficiently. More than that, we redesigned an existing popular flood-modeling method (approved by European Union and used by authorities in Denmark) so that it can efficiently process huge terrain datasets. Given a raster terrain \(\mathcal {G}\) and a subset of its cells representing a river network, both algorithms estimate for each cell in \(\mathcal {G}\) the height that the river should rise for the cell to get flooded. Based on our design, both algorithms can process terrain datasets that are much larger than the main memory of a computer. For an input raster that consists of N cells, and which is so large that it can only be stored in the hard disk, each of the described algorithms can produce its output with only \(O(\mathrm {sort}(N))\) transfers of data blocks between the disk and the main memory. Here \(\mathrm {sort}(N)\) denotes the minimum number of data transfers needed for sorting N elements stored on disk. We implemented both algorithms, and compared their output with data acquired from a real flood event. We show that our new algorithm models the real event quite accurately, more accurately than the existing popular method. We evaluated the efficiency of the algorithms in practice by conducting experiments on massive datasets. Each algorithm could process a dataset of 268 GB size on a computer with only 22 GB working main memory (twelve times smaller than the dataset itself) in at most 31 h.
MADALGO—Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation.
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Notes
- 1.
This is not the case for other types of floods, which have received ample attention in this context [7].
- 2.
Some implementations of \( ProximityFlood \) include an extra preprocessing step where the heights of the river cells are adjusted to make it consistent with the rest of the terrain data. This prevents artifacts (e.g. rivers that flow upstream) that may appear when river data are combined with DEMs acquired from a different sources. In our description of \( ProximityFlood \) we do not include this preprocessing step; we consider that this has to do more with configuring the datasets rather than with the method itself. Yet, this step can be also handled I/O-efficiently, given realistic assumptions on the memory size.
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Alexander, C. et al. (2016). Computing River Floods Using Massive Terrain Data. In: Miller, J., O'Sullivan, D., Wiegand, N. (eds) Geographic Information Science. GIScience 2016. Lecture Notes in Computer Science(), vol 9927. Springer, Cham. https://doi.org/10.1007/978-3-319-45738-3_1
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