Flood monitoring and forecasting using synthetic aperture radar (SAR) and meteorological data: A case study
Bibliographic record
Abstract
Availability of several space-borne synthetic aperture radar (SAR) missions has widened the scope of utilizing radar images for monitoring flooded areas. In this paper, the capability of SAR data was investigated to assess and map flooded regions in Sylhet, located in the northeast part of Bangladesh. Co-polarized (VV) Satellite imageries from 2017 have been collected from Sentinel-1A and Sentinel-1B to use in this study. Relative Humidity (RH), Soil moisture and the amount of precipitation data have been used to predict spatiotemporal inundation in Sylhet region. Digital Elevation Model (DEM) was implemented to forecast the runoff directions of water from mountain after heavy rainfalls in Sylhet region. Results of this study indicated that temporal flood prediction errors could be minimized especially for shorter lead times and overall, they showed the applicability of SAR which in combination with images from SAR, DEM and meteorological data that could be exploited to monitor the flooded areas and give better forecasts.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".