A 3D Irrigation Canal Alignment Optimization Model for a Steep-Sloping Area with Rectangular Inclined Drops
Bibliographic record
Abstract
Traditional optimization approaches for irrigation canal design have primarily focused on identifying cost-effective structural dimensions for simple cross-sections. However, these methods are inadequate for steep terrains where the construction of single or multiple rectangular inclined drops (RIDs) becomes essential. This study introduces a novel three-dimensional optimization model tailored for the optimal design of irrigation canals in such challenging environments. By integrating geospatial data with a particle swarm optimization (PSO) algorithm, the model establishes a continuous search space that facilitates the identification of cost-effective alignments while satisfying hydraulic and construction constraints. To validate its effectiveness, the model was applied to two synthetic case studies that feature varied terrain and slope conditions. Results demonstrated the model's strong capability in optimizing both canal alignment and RID placement. Comparative analysis with genetic algorithm and ant colony optimization revealed that PSO outperformed both in terms of solution accuracy and consistency. Moreover, the proposed model produced results comparable to conventional design methods but with significantly reduced computational time. In addition, pre-cost-estimation tables were developed for various canal route alternatives and RID configurations, offering practical insights for efficient planning and preliminary design of irrigation canals in complex, sloping regions. Received: 11 October 2024 | Revised: 11 July 2025 | Accepted: 5 August 2025 Conflicts of Interest Manoj K. Jha is an Associate Editor for Journal of Computational and Cognitive Engineering, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Mehdi Kazemi: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Ebrahim Amiri Tokaldani: Conceptualization, Methodology, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Supervision, Project administration. Manoj K. Jha: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration. Ramesh Rudra: Validation, Supervision, Project administration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".