DEM Resolution Effects on Coastal Flood Vulnerability Assessment: Deterministic and Probabilistic Approach
Bibliographic record
Abstract
Abstract Flood modeling is highly influenced by topography data set. Lower resolution digital elevation models (DEMs) are usually used because of their availability and less computational burden when they are used in modeling applications. However, low accuracy of these DEMs yields to even lower accuracy in flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed for flood inundation modeling utilizing hydrologically connected bathtub method. High‐resolution light detection and ranging (LiDAR) DEM is considered as the most accurate data from which different resolution maps are obtained using resampling techniques. This is incorporated into an error analysis framework along with U.S. Geological Survey (USGS) National Elevation Dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo‐based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, flood inundation probability at each grid cells is obtained. Furthermore, using the concept of accuracy‐efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be provided, improving flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".