Application of Remote Sensing and GIS to Model Mountainous Rivers
Bibliographic record
Abstract
The purpose of this study is to develop a semidistributed, physically based hydrologic model (SDPB_HM) for mountainous watersheds areas. Most of the model’s required parameters were acquired using remote sensing and digital terrain elevation data. A three-stage computer classifier model was built based on the error back-propagation artificial neural network approach (EBPANN) and refined to classify the land cover types of the study area. In addition, the reflection properties of the land cover types were used to improve the technique of estimating the net radiation in Morton’s evapotranspiration model. A procedure was proposed for discretizing the watershed areas, aimed to increase the homogeneity and minimize the calculation time. The SDPB_HM was applied to the Albert River Basin in the Rocky Mountains in British Columbia, Canada. The Albert River meteorological data from October 1986 to September 1987 was used to calibrate the model parameters. In addition, the SDPB_HM was validated using the meteorological data of a case study from October 1987 to September 1988 and from October 1988 to September 1989. Comparison between the simulated and the observed flow at the outlet of river showed a good agreement during these periods.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".