Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models
Bibliographic record
Abstract
ABSTRACT Floods are one of the most devastating natural hazards, causing adverse effects on human life, well‐being, property, and the environment. The application of five machine‐learning techniques in pluvial flood susceptibility mapping was investigated using the case study of two severe storms (2005 and 2013) in Toronto, Canada. Sixteen flood conditioning factors, including elevation, slope, topographic wetness index, stream power index, amount of permeable and impermeable surfaces, and more, were used to evaluate their importance in terms of flooding impacts for the 2005 and 2013 severe storms. Extreme gradient boosting (XGBoost) and an ensemble method are identified as the best models for the tracks of severe storms in 2005 and 2013. The AUROC (Area under the Receiver's Operating Characteristic Curve) analysis shows that precipitation was the most critical variable, followed by groundwater level and distance from sewers, during the two major storm events investigated. However, the flood susceptibility maps are specific and depend on the storm track and intensity‐duration characteristics for each significant storm event. Depending on the seasonal groundwater levels and the storm sewer drainage capacity of an area, the system may be overwhelmed, and houses may be flooded if the rainfall intensity and duration exceeds the urban stormwater drainage system capacity. This research provides a foundational understanding of the factors influencing urban flood risk and the statistical models that result from pluvial rainfall events. However, there is a need for more research on rainfall events with different tracks, intensities, and durations to provide reliable ensemble flood susceptibility mapping that could be used to calculate the flood risk for a given area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".