GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914488265998336 |
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| author | Sheikh, Muhammad Umer Shehzad, Khawar Khan, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris |
| author_facet | Sheikh, Muhammad Umer Shehzad, Khawar Khan, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris |
| contents | Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools. We present the GCA framework, which unifies (i) GCA-DS, a curated multimodal dataset grounded in the GCC states, and (ii) Gulf Climate Agent (GCA), a tool-augmented agent for climate analysis. GCA-DS comprises 200k question--answer pairs spanning governmental policies and adaptation plans, NGO and international frameworks, academic literature, and event-driven reporting on heatwaves, dust storms, and floods, complemented with remote-sensing inputs that couple imagery with textual evidence. Building on this foundation, the GCA agent orchestrates a modular tool pipeline grounded in real-time and historical signals and geospatial processing that produces derived indices and interpretable visualizations. Finally, we benchmark open and proprietary LLMs on climate tasks in the GCC states and show that domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12306 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support Sheikh, Muhammad Umer Shehzad, Khawar Khan, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris Machine Learning Artificial Intelligence Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools. We present the GCA framework, which unifies (i) GCA-DS, a curated multimodal dataset grounded in the GCC states, and (ii) Gulf Climate Agent (GCA), a tool-augmented agent for climate analysis. GCA-DS comprises 200k question--answer pairs spanning governmental policies and adaptation plans, NGO and international frameworks, academic literature, and event-driven reporting on heatwaves, dust storms, and floods, complemented with remote-sensing inputs that couple imagery with textual evidence. Building on this foundation, the GCA agent orchestrates a modular tool pipeline grounded in real-time and historical signals and geospatial processing that produces derived indices and interpretable visualizations. Finally, we benchmark open and proprietary LLMs on climate tasks in the GCC states and show that domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines. |
| title | GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.12306 |