Development of a HAND-based flood risk assessment tool in Google Earth Engine for a data-scarce region in the US
Bibliographic record
Abstract
Despite the decreased disaster resilience of rural communities in the Great Lakes region to flooding, flood mitigation efforts have been impeded by inadequate data and lack of appropriate tools for understanding flood risk. Development of such resources often requires data and computationally intensive approaches, which are challenging in data-scarce conditions. This study presents the development of a web application in Google Earth Engine (GEE) for flood risk assessment. The application utilizes the Height Above the Nearest Drainage (HAND) model and synthetic rating curve (SRC) for fluvial flood inundation modeling, the Simulating WAves Nearshore (SWAN) model for coastal flood inundation modeling, the United States Geological Survey (USGS) regional regression equations for estimating peak discharge, and depth-damage functions of the HAZUS-MH flood model for estimating losses due to building-level impacts. The GEE-based geospatial web application, which is operational across five counties in the Western Upper Peninsula (WUP) of Michigan, fulfills the requirement of the community and decision-makers to assess the risks caused by flooding in the region. We demonstrated the applicability of the tool in the Ontonagon River, Michigan, and the results indicate the suitability of the platform for implementing decisions, long-term planning, and understanding flood risk with a reasonable degree of accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".