Reevaluating FMOLP Decision Variable Coefficients Using the SWAT Results for the Optimization of Sustainable Agricultural Land Use in Small Watershed
Bibliographic record
Abstract
The inappropriate use of agricultural land has caused several environmental and socio-economic problems. Fuzzy multiple objectives linear programming (FMOLP) is a mathematical technique that can be effective in land use planning. It requires decision variable coefficients (DVCs) to formulate the problem model covering all environmental and socio-economic aspects. The solution from a formulated FMOLP model provides optimal land use proportion and then the proportion is relocated t o produce the land use planning map. The map is then transferred to the soil and water assessment tools (SWAT) for assessing the impacts. The SWAT results give out a value which differs from the initial DVCs in the comparable aspects. This difference indicates that the initial DVCs were not realistic for the selected watershed and the land use planning map were not optimal. This study attempts to reevaluate DVCs in the FMOLP model by designing a method of sustainable agricultural land-use planning which makes use of the outputted results from the SWAT model. The results in terms of soil loss, crop water consumption, crop yields and net profit are simulated by SWAT and then replace to the DVC’s value. This procedure will be repeated until a small difference between SWAT results and DVC’s values are obtained. The findings of this study show that the final land use map achieves a higher target value than the map that was constructed after the initial phase of testing in all of objective. It is hoped the outcomes of this loop back process can be linked to the optimizing technique, so that environmental models can be utilized in the model output to improve optimum proportional solutions in any decision support system (DSS) for future sustainable agricultural land use planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".