Self-optimization of falaj irrigation using case-based reasoning algorithms
Bibliographic record
Abstract
This paper presents a novel application of case-based reasoning (CBR) for modernizing traditional falaj irrigation systems in arid regions, using a multi-level hierarchical framework that addresses challenges at provider, tenant, and user levels. The research employs a comprehensive methodology that integrates traditional water management practices with modern technologies while preserving cultural heritage. Through the implementation of CBR at Falaj Al Sarrani, the study demonstrates significant improvements in water conservation (58.3% reduction in water use), crop productivity (27.3% average yield increase), and economic returns (23.7% internal rate of return). The research evaluates five similarity functions across hierarchical levels, identifying optimal functions for each level: Manhattan distance for the provider level, Squared Chord for the tenant level, and Canberra for the user level. This level-specific optimization reduced the overall system error rate by 18% compared to using any single function across all levels. The findings provide valuable insights for water resource managers, agricultural agencies, and policymakers facing water scarcity challenges in arid and semi-arid regions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".