Application of Soil Water Assessment Tool (SWAT) to suppress wildfire at Bayam Forest, Turkey
Bibliographic record
Abstract
Readily available water resources are a key for wildfire suppression.Hydrologic models are a practical and essential tool for understanding the processes of hydrology and managing water resources, but have not been utilized as frequently for wildfire suppression.The goal of the present study was to use the Soil Water Assessment Tools (SWAT) model to determine whether the stream water could be managed sustainably in wildfire suppression at the Bayam Forest District in Kastamonu Province, Turkey.As an input file, the SWAT model used soils, land-uses, weather data and morphology of watershed based on the Digital Elevation Model (DEM).The model was applied for period 2001-2013 in order to predict the water budget of the study area and major streams within the studied district.T h e a n a l y s i s o f t h e hydrologic water budget indicated that 70% (573.8 mm) of t h e a n n u a l precipitation (822 mm) was lost as evapotranspiration in the basin, whereas 19%, 34% and 47% of the remaining total water yield (234.6 mm) contributed to streams via surface runoff, groundwater flow and lateral flow, respectively.Overall, the result of S W A T m o d e l indicated to a certain degree promising f i n d i n g s o n t h e availability of stream water and optimal placement of water reservoir for the use of wildfire suppression.
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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 | 0.001 | 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".