Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries
Bibliographic record
Abstract
According to a United Nations report, the world population will increase from 7 billion to 9 billion by 2050. Further, the water stress level is more than 70% in 22 countries while in another 31 countries it is between 25% and 70%. More than 2 billion people live in these 53 countries which are all underdeveloped. Water use has increased by 1% per year since the 1980s, so global demand is expected to rise by 30% by 2050. Thus, efficient water grid management is imperative to ensure there is sufficient water for the future. Information and Communication Technology (ICT) can be used to create smart water grids to optimize water distribution, reduce waste and leakage, and resolve quality and overuse issues. In this work, a low cost, real-time, reliable and sustainable IoT based solution called SmartTubewell is proposed for smart water grid management. It is composed of two components, a sensor node installed at tube wells and an application layer on Amazon Web Services (AWS) for data analysis, storage and processing. The sensor node is based on a Raspberry Pi with integrated current and voltage sensors and a local database. The sensor data is transmitted to AWS using a cellular (GPRS) network. A comparison between the proposed system and SCADA is presented which shows that SmartTubewell has a much lower cost. A field test with multiple tube wells in Peshawar, Pakistan indicates that this is a suitable solution for developing countries.
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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) | 0.000 | 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".