A Review of Artificial Intelligence Applications to Achieve Water-related Sustainable Development Goals
Bibliographic record
Abstract
This paper reviews the Artificial Intelligence (AI) applications that help achieve water-related Sustainable Development Goals (SDGs). Current applications of AI in the water sector include i) predictive maintenance of water infrastructure, ii) forecasting water demand and consumption, iii) monitoring water reservoirs and dams, iv) tracking water quality, and v) monitoring and predicting water-related disasters. These applications contribute to achieving water-related SDG targets, specifically 3, 6, 11, and 15. The literature review shows that: i) the rate of adoption of AI-based solutions in predictive maintenance of water infrastructure has accelerated, as AI becomes increasingly accessible, and data analytics and smart sensors become more efficient and affordable; ii) deep learning technology has enabled a new generation of water management systems, which can generate short-term (daily) and long-term (annual) forecasts. iii) as Asia and South America experience an increase in water reservoir and dam construction, AI-based techniques are being successfully implemented in reservoir development and operation; iv) water quality monitoring has been the most significantly impacted by AI relative to other applications, as AI is used to examine small samples and large water bodies, and for real time water quality monitoring; v) AI can be used to forecast water-related disasters with higher accuracy, frequency and lead time, allowing for focused management of post-disaster activity. The paper ends by highlighting the challenges of adopting AI to achieve water-related SDGs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".