Research on Resource Optimization Path of Agricultural Water-saving Irrigation Management Supported by Intelligent Algorithms
Bibliographic record
Abstract
In order to improve the efficiency of agricultural irrigation industry and ensure the economy and environmental protection in the production process.The study proposes an agricultural water-saving irrigation path optimisation method based on the NGSA-III algorithm, and establishes a multiobjective water-saving optimal allocation model for the agricultural water source irrigation system.The NGSA-III algorithm is used to obtain the optimal solution of the model and achieve the path optimisation of agricultural water-saving irrigation resources.The results show that the running time of the article method to get the optimal path result is 0.31s, which can improve the economic and environmental benefits of the agricultural irrigation industry, the model in this paper can achieve the effect of smaller environmental objectives when the economic objectives are larger, and three solutions are selected to trade-off the analysis of economic and environmental objectives.Among the three different optimal solutions, the decision maker can choose the decision scheme according to the actual situation, which provides reference for agricultural water saving path 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".