Publication2026
Air Pollutants Estimation Paradigm in Urban Sensing Zones Using Enhanced Random Forest Approach
- Good Health and Well-being
- Climate Action
- Thriving and Sustainable Economy
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1775 LNNS
- Pages
- 220-230
- Published
- 1 January 2026
- DOI
- 10.1007/978-3-032-14189-7_20
- Scopus ID
- 105028887049
- ISSN
- 23673370
- Authors
- 4
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
13 Take urgent action to combat climate change and its impacts
Machine learning approach for predicting air pollutant concentrations in urban environments using sensor data, enabling effective air quality management and pollution monitoring.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
3 Ensure healthy lives and promote well-being for all at all ages
Air quality monitoring supports health outcomes by enabling timely interventions and policy responses to reduce health impacts from air pollution.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2 Thriving and Sustainable Economy
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 13 label. Machine learning approach for predicting air pollutant concentrations in urban environments using sensor data, enabling effective air quality management and pollution monitoring.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Air Pollutants Estimation Paradigm in Urban Sensing Zones Using Enhanced Random Forest Approach”. Kortex.