With Canada's increasing population, natural disasters such as flooding events will have
an increasing impact on human populations. The severity of these events requires that decision
makers have a clear understanding of the flood risks that communities face in order to plan for
and mitigate flood risks. One key component to understanding flood risk is flood exposure, an
element of which is the presence of structures (e.g., residences, businesses, and other buildings)
in an area that could be damaged by flooding. Presently, several resources exist at both the
national and global level that can be used to estimate the spatial distribution of structures. These
resources are typically generated at global scales and do not account for regional or local data or
processes that could enhance the accuracy and precision of exposure estimation in sparsely
populated areas. The present study investigates the feasibility of creating a region-specific
dwelling distribution model that helps improve estimation of residential structures in rural areas.
Herein, we describe a rural dwelling distribution model for the province of Alberta that can be
used to assist in the estimation of structural exposure to flood risk. The model is based on a
random forest classification algorithm and several publicly available datasets associated with
dwelling and population density. The model was validated using visually referenced data
collected from earth imagery. The resulting dwelling layer was then evaluated in its ability to
spatially disaggregate census dwelling counts, as well as predict dwelling exposure in several
scenarios. This method appears to be a useful alternative to globally scaled models, or using the
census alone, particularly for rural areas of Canada. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25866 |
| Date | January 2020 |
| Creators | Kurani, Sami |
| Contributors | Yiannakoulias, Niko, Geography |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds