Generating Topobathymetry Digital Elevation Model using Crowdsourced Bathymetry: A case of the St. Lawrence River and Ottawa River in Quebec
Bibliographic record
Abstract
The accuracy of two- and three-dimensional hydraulic modelling of free surface flow depends significantly on a complete and accurate geometric description of the river channel and floodplains in the form of a continuous, seamless digital elevation model (DEM). With the advent of airborne Light Detection and Ranging (LiDAR) surveys, high-resolution topographic data is increasingly becoming available. However, bathymetric information for most rivers is not available in ready-to-use digital data formats, mainly because the primary data collection methods, i.e., hydrographic surveys, are costly and time-intensive. The existing methods for generating topobathymetry digital elevation model (TB-DEM) require access to raw data and ground measurements to some extent. This study proposes a simple superposition-based approach to generating a seamless elevation model using terrestrial and bathymetry information available from secondary data sources, including crowdsourcing. It comprises geographic information system (GIS) based interpolation and geoprocessing techniques. An integrated TB-DEM is generated for part of the St. Lawrence River and Ottawa River and the overbank areas on the upstream side of Montreal Island in Quebec. The output DEM is verified using internal and external validation criteria. The upland topography is unaffected by the superposition process, whereas the interpolated bathymetry shows significant positive linear associations with the reference elevation data. The vertical accuracy of bathymetry DEM with respect to Canadian Hydrographic Service Non-Navigational Bathymetric Data-10 (NONNA-10) reference data is 1.43 m in root-mean-squared error. The results of 1-m × 1-m DEM from this study are useful for evaluations of fish habitat health, shoreline stability and drinking-water withdrawal-site selection, and for predictions of river floods, morphological changes, and changes of water quality. The methods are applicable to other sites for generating high-resolution DEMs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".