| Title | Harmonizing and extending fragmented 100 year flood hazard maps in Canada's capital region using random forest classification |
| |
| Author | Bhuiyan, S A ;
Bataille, C P; McGrath, H |
| Source | Water vol. 14, issue 23, 3801, 2022 p. 1-18 |
| Image |  |
| Year | 2022 |
| Alt Series | Natural Resources Canada, Contribution Series 20220028 |
| Publisher | MPDI |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | hydrogeology; 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 |
| Illustrations | location maps; satellite images; tables; flow diagrams |
| Program | Canada Centre for Remote Sensing Flood Mapping Guidelines |
| Released | 2022 11 22 |
| Abstract | With 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. |
| GEOSCAN ID | 331450 |
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