Using thermal remote sensing as a tool for calibrating a hydrodynamic model in inland waters
Bibliographic record
Abstract
Remote sensing has been proven as an effective tool for mapping and monitoring water quality in coastal/inland waters during the past two decades. In light of this, it can also be applied to calibrate hydrodynamic models which predict the distribution of river plumes and streams in coastal/inland waters. This research examines the capability of Landsat 7 thermal data to calibrate a 3D hydrodynamic model by simulating a moderate sized river plume discharging into Lake Ontario, USA. The model is provided with a set of input variables and involves modeling material transport using a finite-differencing method to generate profiles of temperature within the water column as well as a surface temperature map. In this way, a Look-Up-Table (LUT) of multiple scenarios of environmental conditions was built by running the hydrodynamic model for several simulation hours. This process resulted in various shapes of the thermal plumes, one of which represented the best output. This was determined by making a comparison with atmospherically compensated Landsat 7 thermal data in the surface temperature domain. The best agreement with the remotely sensed data was found through an optimization in which an error function, calculated between the model outputs and the imagery, was minimized. The root-mean-squared-error (RMSE), computed between the best model output and the observed imagery on a pixel-by-pixel basis, indicated a good fit with less than half of a degree, approximately 0.34° C, on average, over the plume area. This research demonstrates the potential of existing Landsat data and the corresponding method to monitor river plumes of moderate size in inland/coastal environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".