Design of a Groundwater Level Monitoring System Based on Internet of Things and Image Recognition
Bibliographic record
Abstract
This paper designs an intelligent groundwater level monitoring system based on image recognition and Internet of things (IoT). Image recognition technology was employed to process the water level image, and determine the water level line. The IoT was adopted to transmit the collected multimedia data accurately to the monitoring end, thereby realizing the automatic remote monitoring of real-time water level. After analyzing the image recognition technology and the key algorithm of water level recognition, the authors designed the whole process of groundwater level monitoring with two modules: water level monitoring base station, and remote monitoring management center. The water level monitoring base station is embedded with a data acquisition module to periodically collect data, including water level, videos, and images. The collected data were sent to the remote monitoring management center through the cellular network. Then, flood or low water warning could be determined according to the historical data. Finally, the proposed groundwater level monitoring system was tested. The results show that the system not only solves the problem of measurement accuracy, but also improves the work efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".