Evaluating flood exposure for properties in urban areas using a multivariate modelling technique
Journal article, Peer reviewed
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Original versionWater. 2017, 9, (5), . 10.3390/w9050318
Urban flooding caused by heavy rainfall is expected to increase in the future. The main purpose of this study was to investigate the variables characterizing the placement of a house, which seem to have an impact when it comes to the exposure to floods. From the same region in Norway, data from 347 addresses were derived. All addresses were either associated with insurance claims caused by flooding or were randomly selected. A multivariate statistical model, Partial Least Square Regression (PLS), was used. Among others, the analysis has shown that the upstream, sealed area is the most significant variable for characterizing properties’ exposure to urban flooding. The model confirms that flooding tends to occur near old combined sewer mains and in concave curvature, and houses located in steep slopes seem to be less exposed. Using this method, it is possible to rank and quantify significant exposure variables contributing to urban floods within a region. Results from the PLS-analysis might provide important input to professionals, when planning and prioritizing measures. It can also predict flood-prone areas and make residents aware of the risks, which may induce them to implement preventive measures.