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TitleHarmonizing and extending fragmented 100 year flood hazard maps in Canada's capital region using random forest classification
AuthorBhuiyan, S AORCID logo; Bataille, C P; McGrath, HORCID logo
SourceWater vol. 14, issue 23, 3801, 2022 p. 1-18
Alt SeriesNatural Resources Canada, Contribution Series 20220028
Mediapaper; on-line; digital
File formatpdf
Subjectshydrogeology; geophysics; Nature and Environment; Science and Technology; Health and Safety; floods; mapping techniques; models; modelling; water levels; hydrodynamics; remote sensing; satellite imagery; Methodology; Artificial intelligence
Illustrationslocation maps; satellite images; tables; flow diagrams
ProgramCanada Centre for Remote Sensing Flood Mapping Guidelines
Released2022 11 22
AbstractWith the record breaking flood experienced in Canada's capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada's aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.
Summary(Plain Language Summary, not published)
Testing a Machine learning approach to create flood hazard maps in areas where there is no existing mapping, using provincial flood maps as training (input) data.

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