Publication2026
Climate-Sensitive Modeling of Rice Stock in Odisha Using Hybrid Machine Learning and Explainable AI
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- IEEE Access
- Volume
- 14
- Pages
- 28785-28805
- Published
- 1 January 2026
- DOI
- 10.1109/access.2026.3665921
- Scopus ID
- 105030622861
- ISSN
- 21693536
- Authors
- 3
- Open access
- Yes
- Citations
- 1
- 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
2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture
District-level climate-aware hybrid ML forecasting of rice stock dynamics under agro-climatic variability, supporting food production resilience in vulnerable regions.
- 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
13.1 Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters
Addresses climate-sensitive agricultural production forecasting with monthly agro-climatic variables to support adaptive responses to climate variability.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Climate-aware modeling for rice stock optimization in agricultural production directly supports rural economy and agricultural resilience.
- 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. “Climate-Sensitive Modeling of Rice Stock in Odisha Using Hybrid Machine Learning and Explainable AI”. Kortex.