LLMAir: Adaptive Reprogramming Large Language Model for Air Quality Prediction

Accurate and timely air quality prediction is crucial for cities and individuals to effectively take necessary precautions against potential air pollution. Existing studies typically rely on building prediction models based on large-scale monitoring data, often designed for specific tasks. Recently,...

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Veröffentlicht in:	Proceedings - International Conference on Parallel and Distributed Systems S. 423 - 430
Hauptverfasser:	Fan, Jinxiao, Chu, Haolin, Liu, Liang, Ma, Huadong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 10.10.2024
Schlagworte:	Air quality Air quality prediction Analytical models Atmospheric modeling Data models Large language models Monitoring Predictive models Regulators Semantics Spatiotemporal phenomena Urban data analysis
ISSN:	2690-5965
Online-Zugang:	Volltext
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