Optimal sensor placement for agro‐hydrological systems
Bibliographic record
Abstract
Abstract The estimation of soil moisture is essential for developing advanced closed‐loop irrigation schemes. One associated problem is how to place the sensors appropriately in the soil to provide good measurements for state estimation. In this work, we address the problem of optimal sensor placement for state estimation of agro‐hydrological systems. A systematic approach is proposed to find the minimum number of sensors that ensures the observability of the entire system and then to find the best locations of the sensors in terms of degree of observability. The Richards equation that is used to describe the dynamics of the agro‐hydrological system is discretized into a large‐scale nonlinear state‐space model. In the proposed procedure, the key steps include order reduction of the large‐scale system model, exploration of the minimum number of sensors needed for state estimation and optimal placement of the sensors in the soil. Three different scenarios are considered and optimal sensor placement is addressed for all the scenarios using the proposed procedure. Simulation results show the effectiveness of the proposed procedure and methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".