4 Results and discussion
4.3 Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate
In this study, terrain and sewer parameters associated with flooded and non-flooded addresses, were investigated. All addresses were associated with the Building Central Point (BCP) obtained from the Norwegian cadaster. The aim was to reveal patterns and correlations between characteristics of location of a house and the impact of flooding. From the dataset it
29 was built a model to determine whether a house, based on the terrain and sewer data of that particular address, could be identified as flooded or not, without having knowledge of previous flood events. As the latter was formulated as a classification issue with two response variables (flooded/non flooded), Partial Least Squares – Discriminant Analysis (PLS-DA) was found suitable for this study
Two classes, flooded (F) and random (R), were defined. Full name and a brief description of each variable, as well as average and Standard Deviation-values (SD), are shown in Table 1 in Paper III.
Figure 6: Scores (upper) and loading (lower) plot computed from PLS-DA
The output from the PLS-DA model in terms of score and loading plot is shown in Figure 6.
30 From the model, the first two factors (latent variables) in sum described 27% and 36% of the variance in the dataset, for X and Y respectively.
From the score plot, we see the red-marked dots (F) are mostly located to the right (positive value of Factor-1), while most of the random data is to the left. The separation between the red and blue dots indicates the different structure of the two classes. This difference is mainly explained by Factor-1. It is hard to discriminate the classes along the Factor-2 axis (or any other factors at higher level). The loading plot in Figure 6 shows the importance of each variable for Factors 1 and 2, respectively.
The flooded addresses (F) are rightmost in the score plot. Simultaneously, impervious and upstream area surrounding BCP (variables starting with ‘a_U’ in the loading plot in Figure 6, see Table 1 in Paper III for further details) are to the right in the loading plot. This indicates that these variables are significant for flood-prone properties.
Variables describing distance to the sea (d_C) seemed to have relatively little influence, as it in the loading plot was located close to the centre of Factor-1. Elevation (d_z) turned out to have more impact as this variable is plotted to the left along the Factor-1 axis and inversely correlated with flood-prone homes. Terrain curvature determines whether a given part of a surface is convex or concave. For plan curvature and profile curvature, the sign rules are inversely defined. A negative and a positive number respectively describe concavity. Profile curvature indicates the shape of the surface in the steepest direction, and if the terrain flattens into a concave curvature, the flow will currently slow down and the water level will rise. In this study, flooded addresses (F) can be associated with a concave profile curvature (variables starting with ‘c-pr’) as they are located to the right side in both plots in Figure 6.
Unlike urban floods, another flood-type, flash flood, occurs when heavy rain is falling on the slopes. The water will immediately drain to rivers that hold little or no water. This can be potentially dangerous, since it causes a sudden rise in the water level of the river that is difficult to forecast. In 2003, a so-called Flash Flood Potential Index (FFPI) was presented.
Originally, this index was based on the parameters slope, land use, soil type and vegetation cover. Slope was given a slightly higher weight than the other parameters that were weighted equally. (Smith, 2003). Later the model has been developed and high slope as well as urban areas have been emphasized (Zogg and Deitsch, 2013). A comparison between the FFPI and the study reported here, illustrates differences between the two flood types. As steep slopes are characteristic of areas with high FFPI, this study showed that little slope give increased potential for flood related damages in urban areas. A possible outcome of this study can be development of an index that characterizes an address’ potential exposure to urban floods, based on the most significant variables. Furthermore, this can be put into a GIS-tool along with other flood characteristics for production of a comprehensive risk map.
Limitation/Validation of this study
This work showed that PLS-DA is a suitable tool to predict the flood-prone nature of a property area. However, as can be seen from Figure 6, only 36% of the variance in the responses was captured by the two first factors of the model. This can be regarded as an applicable value for a ‘non-laboratory investigation’, even if much of the variance is not captured by the model.
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Even though the results from this study seemed reasonable and justified by hydraulic principles, they are restricted to this case area and for a particular time period. A larger dataset and data from other cities would have strengthened the conclusions. Manual methods used to extract sewer data can be a source of error. Automatic methods should be developed to reduce the possibility of human error. Despite this, there were no indications of incorrect data due to manual methods.
A simplification was made for calculation of the size of the upstream area, as all cells above the BCP level were included. However, an area of higher elevation than BCP within a given zone, does not necessarily mean that water is drained through BCP. Still, the same procedure was used for all addresses, and there were no indications that this simplification led to bias in the model.
When extracting terrain variables, the locations of BCP may be anywhere within the cell, not necessarily in the centre of the house. To overcome this problem, some terrain variables were derived from the weighted mean of the four nearest pixel values. The variables taking nearest cells into account are referred to as interpolated values and end with ‘ip’ in the loading plot in Figure 6. As can be seen from this plot, these interpolated values are close to variables based on values from one single cell.
As mentioned in Paper III, this study had a link to the sewer system. Thus, ca 15% of the addresses were excluded from the sample as the damage could not likely be associated with the sewer system.
The PLS-DA model was validated by cross validation. Using the validated predicted values for classification by applying the ‘winner-takes-all-strategy’ as described in Paper III, 84% of the initially flooded houses were correctly classified.
Outcome from this study
The overall results from this study revealed some distinctive characteristics for the most exposed properties in the case area in Fredrikstad during 2006-2012:
Houses in flat areas or concave curvature are more exposed.
Houses on plots with a large upstream area are more exposed.
Houses in steep slopes are less exposed.
In a broader perspective, this model can:
be a useful tool when planning new sites
quantify and rank an address’ exposure to floods based on objective criteria
make people aware of the risk of floods
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