Modeling of Runoff in the Arid Regions Using Remote Sensing and Geographic Information System (GIS)
Bibliographic record
Abstract
One of the most important challenges in the field of engineering hydrology and water resources management, especially in arid regions such as the Iraqi Western Desert, is the process of predicting and quantifying the surface runoff. The limited available data about rainfall, runoff, soil properties, evaporation, and the lack of metrological stations make the process of predicting and calculating surface runoff a very difficult task. Modern technology can help with the purpose of compensating for the shortage of data and providing the information necessary to estimate the runoff and develop the system of water resources management in the region. The present study develops a model to determine the infiltration of soil from spectral reflectance using Artificial Neural Networks (ANN) integrated with a geographic information system (GIS) and remote sensing (RS). Field infiltration measurements for 105 soil samples in the Al-Ratga catchment area in the Iraqi western desert are achieved. The performance of the developed model was assessed both qualitatively and quantitatively (effective runoff depth) by comparing the results of actual and estimated basic infiltration rate values for each sample. The results refer to a good agreement between estimated and measured infiltration (R2=0.768). The developed model predicts the runoff depending on the water balance equation and the results refer to good agreement with the SCS-CN model that is one of the most widely used in this region.
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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 | 0.000 | 0.001 |
| 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".