A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring
Bibliographic record
Abstract
Real-time and accurate measurement of the water level is a critical step in flood monitoring and management of water resources. In recent years, with the advent of the Internet of Things (IoTs) and cloud computing platforms and resources, the surveillance technology for water monitoring has been revolutionized due to the availability of high-resolution and portable cameras, robust image processing techniques, and cloud-enabled data fusion centers. However, despite the potential advantages of online water level monitoring of the rivers and lakes, some technical challenges need to be addressed before they can be fully utilized. Submersible sensor devices are frequently used for measuring water levels but are prone to damage from sediment deposition and many gauge detection techniques are inefficient at nighttime. In response, this paper presents a novel Internet of Things (IoT) based deep learning methodology that uses Mask-RCNN to accurately segment gauges from images even when there are distortions present. An automated and immediate water stage estimate is provided by this simple, low-cost method. The methodology’s applicability to water resource management systems and flood disaster prevention engineering opens up new possibilities for the deployment of intelligent IoT-based flood monitoring systems in the future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".