Spatial Modeling of the Al-Ula Basin in the Medina Region, Saudi Arabia Using GIS
Bibliographic record
Abstract
GIS techniques change the General Approach to the validation of hydrological models considerably. These techniques have facilitated accurate verification and calculation of all phenomena used to describe the hydrologic basins. The study area consists of a group of valleys affecting Al-Ula city, and the data modelling was presented using the relational method, as an integrated network of geographical phenomena that provides a simpler way to display the concerned modeling, which enables understanding the geographical data when processing and spatially representing the basins. The study identified the cadastral characteristics of the study area; the total basin area was approximately 988,33 km 2 , while the water basins ranged from 32,23 km 2 to 333,93 km 2 . After in-depth analysis of the topographic variables, the study found that the steepest basin in the study area was the B1 basin at about 12.65%, while the B5 basin was the lowest basin at 9.46%, and the lowest peak flow in the water network was about 105.51 m 3 /s in the B1 basin due to short length of watercourses; this means that the basin has not reached a wave-cut activity as it continues to expand. The maximum peak flow in the water network reached 369.82 m 3 /s in basin B5, which may help increase the waterways in the basin; this, in turn, is followed by increased risks of runoff. The study increasingly recommended the use of geographic information systems (GIS) to enhance land capabilities, by interpreting the cadastral, topographic and hydrological variables of the basins in the study area. Therefore, GIS as a powerful spatial system could be used to develop and calibrate models and spatially present their results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".