Surface Water Area Detection And Extraction By Using Different Techniques Of Remote Sensing And GIS.
Bibliographic record
Abstract
Jayakwadi dam built in 1976, is located in Jayakwadi village in Paithan taluka of Chhatrapati Smbhaji Nagar district in Maharashtra, India. Monitoring the surface water area of Jayakwadi Dam is important task because the main purpose of dam water is for drinking water as well as supply to industrial area for Chhatrapati Sambhaji Nagar city people and irrigation. The surface water area can be easily estimated with the help of satellite imagery and remote sensing and GIS technique. The data used to estimate surface water area is Landsat-8 satellite multispectral data. Landsat-8 has resolution of 30 Meter. We used mainly data of 8 Years from 2014 to 2022. NDWI and Maximum likelihood classification technique to estimate surface water area. As compare to maximum likelihood classification, NDWI and water pixel extraction is more accurately calculate the surface area of water reservoir. In this paper the maximum likelihood classification as well as NDWI method comparatively used to find out the surface water area by satellite image.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".