Digital water: artificial intelligence and soft computing applications for drinking water quality assessment
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It requires significant effort to convert data schemes.
“It also requires significant effort to convert data schemes [36 -39].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [39]
The citation lists haven’t been fetched yet
References 135 · Cited by 46
- Authors
- Chhipi-Shrestha, G., Mian, H. R., Mohammadiun, S., Rodriguez, M., Hewage, K., & Sadiq, R.
- Affiliations
- University of British Columbia, Okanagan Campus (CA)
- Corresponding author
- Mian, Haroon R.
- Venue
- Clean Technologies and Environmental Policy
- Published
- 2023-02-02
- Volume
- 25
- Issue
- 5
- Pages
- 1409-1438
- ISSN
- 1618-954X (ISSN-L)
- 1618-9558
- OpenAlex
- W4318994469
- Cited by
- 46
- References
- 135
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 14 · 2023: 8
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Clean Technologies and Environmental Policy (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- drinking water quality, artificial intelligence, soft computing, water quality monitoring, Internet of Things, water quality prediction, water quality control, water utilities, microbiological water quality, physicochemical water quality, smart water systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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