Publication2026
Prediction of Strength of a Storm in Indian Subcontinent Using PCA Technique
- Sustainable Cities and Communities
- Climate Action
- Thriving and Sustainable Economy
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Proceedings of the 2026 13th International Conference on Computing for Sustainable Global Development Indiacom 2026
- Published
- 1 January 2026
- DOI
- 10.23919/indiacom70271.2026.11526074
- Scopus ID
- 105041890038
- Authors
- 3
- 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 with principal component analysis forecasts tropical cyclone strength in Indian subcontinent, enabling enhanced early warning signals and decision-making for disaster risk mitigation in vulnerable coastal states.
- 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
11 Make cities and human settlements inclusive, safe, resilient and sustainable
Hybrid ML-traditional forecasting framework supports resilience building in exposed coastal regions by improving prediction of wind intensity, pressure variations, and storm tracks.
- 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 with principal component analysis forecasts tropical cyclone strength in Indian subcontinent, enabling enhanced early warning signals and decision-making for disaster risk mitigation in vulnerable coastal states.
- 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.
Evidence
Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.
No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 1
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “Prediction of Strength of a Storm in Indian Subcontinent Using PCA Technique”. Kortex.