Low cost, LoRa based river water level data acquisition system
Bibliographic record
Abstract
In recent years, climate change and catchment degradation have negatively affected stage patterns in rivers which in turn have affected the availability of enough water for various ecosystems. To realize and quantify the effects of climate change and catchment degradation on rivers, water level monitoring is essential. Various effective infrastructures for river water level monitoring that have been developed and deployed in developing countries over the years, are often bulky, complex and expensive to build and maintain. Additionally, most are not equipped with communication hardware components which can enable wireless data transmission. This paper presents a river water level data acquisition system that improves on the effectiveness, size, deployment design and data transmission capabilities of systems being utilized. The main component of the system is a river water level sensor node. The node is based on the MultiTech mDot - an ARM-Mbed programmable, low power RF module - interfaced with an ultrasonic sensor for data acquisition. The data is transmitted via LoRaWAN and stored on servers. The quality of the stored raw data is controlled using various outlier detection and prediction machine learning models. Simplified firmware and easy to connect hardware make the sensor node design easy to develop. The developed sensor nodes were deployed along River Muringato in Nyeri, Kenya for a period of 18 months for continuous data collection. The results obtained showed that the developed system can practically and accurately obtain data that can be useful for analysis of river catchment areas.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".