Development of a Cloud-Based Dual-Objective Nonlinear Programming Model for Irrigation Water Allocation
Bibliographic record
Abstract
Irrigation water allocation is essential to the management of agricultural water use in irrigation districts. Many irrigation optimization models were proposed from previous studies to provide decision support for water managers. In order to capture the complex nonlinear relationships and meet different water demands, more advanced multi-objective nonlinear programming models were developed in the past decade. However, it is still a challenging task to address varies uncertainties associated with irrigation optimization. Fuzzy programming, interval programming, and chance-constrained programming can be used to quantify uncertainties in simplified formats, but none of them can represent complex uncertainty in a composite format. In this thesis, a cloud-based dual objective nonlinear programming (CDONP) model is developed by implementing a cloud modeling method in an irrigation model to address the uncertainties of reference evapotranspiration (ET0) and surface water availability (SWA). The cloud modeling method is used to generate 2,000 data samples from historical data. The results show that the generated samples are consistent with historical data. Optimized allocation schemes are provided, and the performance of the CDONP model are discussed. This is the first Canadian study that used the cloud modeling method in irrigation water allocation. This method provides a solution to quantify composite uncertainties based on limited data, which represents a unique contribution to irrigation water allocation modeling. This study provides valuable decision support for agriculture management to improve water use efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".