| Title | Prediction and classification of flood susceptibility based on historic record in a large, diverse, and data sparse country |
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| Author | McGrath, H ; Gohl,
P N |
| Source | Environmental Science Proceedings 25, 1, 2023 p. 1-7, https://doi.org/10.3390/ECWS-7-14235  Open Access |
| Image |  |
| Year | 2023 |
| Alt Series | Natural Resources Canada, Contribution Series 20220185 |
| Meeting | 7th International Electronic Conference on Water Sciences; March 15-30, 2023 |
| Document | serial |
| Lang. | English |
| Media | digital; on-line |
| File format | pdf |
| Province | Canada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut |
| NTS | 1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65;
66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560 |
| Lat/Long WENS | -141.0000 -50.0000 90.0000 41.7500 |
| Subjects | Science and Technology; floods; flood potential; meteorology; machine learning; Meteorological data; Forests |
| Illustrations | tables; location maps; graphs |
| Program | Geobase 2.0 High Resolution Data
Exploitation |
| Released | 2023 03 16 |
| Abstract | The emergence of Machine learning (ML) algorithms has shown competency in a variety of fields and are growing in popularity in their application to geospatial science issues. Most recently, and notably,
ML algorithms have been applied to flood susceptibility (FS) mapping. Leveraging high-power computing systems and existing ML algorithms with national datasets of Canada, this project has explored methods to create a national FS layer across a
geographically large and diverse country with limited training data. First, approaches were considered on how to generate a map of FS for Canada at two different levels, (i) national, which combined all training data into one model, and (ii)
regional, where multiple models were created, based on regional similarities, and the results were mosaicked to generate a FS map. The second experiment explored the predictive capability of several ML algorithms across the geographically large and
diverse landscape. Results indicate that the national approach provides a better prediction of FS, with 95.7% of the test points, 91.5% of the pixels in the training sites, and 89.6% of the pixels across the country correctly predicted as flooded,
compared to 65.5%, 80.6% and 75.6%, respectively, in the regional approach. ML models applied across the country found that support vector machine (svmRadial) and Neural Network (nnet) performed poorly in areas away from the training sites, while
random forest (parRF) and Multivariate Adaptive Regression Spline (earth) performed better. A national ensemble model was ultimately selected as this blend of models compensated for the biases found in the individual models in geographic areas far
removed from training sites. |
| Summary | (Plain Language Summary, not published)
An assessment of the impact of adding meteorological variables on the results of a machine learning approach to creating flood susceptibility
map. |
| GEOSCAN ID | 330433 |
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