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Computer Science > Machine Learning

arXiv:2604.12306 (cs)
[Submitted on 14 Apr 2026]

Title:GCA Framework: A Gulf-Grounded Dataset and Agentic Pipeline for Climate Decision Support

Authors:Muhammad Umer Sheikh, Khawar Shehzad, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris Khan
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Abstract:Climate decision-making in the Gulf 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 Gulf-focused multimodal dataset, 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 Gulf climate tasks and show that domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2604.12306 [cs.LG]
  (or arXiv:2604.12306v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2604.12306

Submission history

From: Muhammad Umer Sheikh [view email]
[v1] Tue, 14 Apr 2026 05:31:40 UTC (2,707 KB)
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