Integrating genetic algorithm with AnnAGNPS for optimizing BMPs placement to reduce sheet/rill and ephemeral gully erosion
Bibliographic record
Abstract
In order to effectively reduce nonpoint source pollutants in agricultural areas within a watershed, a combination of Best Management Practices (BMPs) is selected based on their economic and environmental effectiveness. However, determining the optimal combination can be challenging due to the implementation costs and the consideration of decision makers' preferences. This research presents a methodology for integrating a genetic algorithm with the Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to effectively select the most efficient BMPs placement for a given watershed. By optimizing BMPs placement, the model can minimize sediment loads from different types of erosion, including sheet/rill, ephemeral gully, and total erosion at the minimal cost. Results demonstrated that BMP placement by the optimization model reduced sediment load caused by sheet/rill by 84.6 %, ephemeral gully by 85.4 %, and total erosion by 86.3 % in the study watershed. Additionally, the model achieved these results at a minimal cost, making it a cost-effective solution for sediment load reduction in the watershed. Also, the results showed the effective implementation of the developed optimization approach for strategically locating BMPs in specific areas, rather than implementing them throughout the entire watershed. By targeting these areas and implementing suitable BMPs, the model was able to reduce the amount of sediment load and remain cost-effective. The proposed weighted overlay technique helped to place BMPs within agricultural fields instead of AnnAGNPS cells, making it easier for farmers to adopt and effectively reduce sediment load in each field. The developed model in the current study can be applied by decision makers in other watersheds with limited resources for implementing BMPs. • Integration of genetic algorithm with AnnAGNPS to optimize BMPs placement. • Model reduced total erosion by up to 86.3 % with minimal cost. • Effective BMPs targeting reduces sediment load without watershed-wide implementation. • Weighted overlay technique facilitates easier BMPs adoption for farmers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".