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Large language models for environmental modeling: Framework, capabilities, constraints.

Qiyang Nie1, Tong Liu2

  • 1Graduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, Japan.

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Large Language Models (LLMs) offer new pathways for environmental modeling. A human-AI Copilot framework excelled in parameter calibration and real-time correction, while Autopilot faced limitations.

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Autopilot frameworkCalibrationCopilot frameworkEnvironmental modelingFlood modelLarge language modelReal-time correction

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Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Computational Modeling

Background:

  • Environmental modeling complexity is increasing.
  • Integrating Large Language Models (LLMs) into these workflows presents challenges.
  • Practical frameworks for LLM integration in environmental modeling are needed.

Purpose of the Study:

  • To introduce and evaluate two frameworks for embedding LLMs into environmental modeling workflows: a human-AI collaborative Copilot and an LLM-driven Autopilot.
  • To assess the performance of these frameworks in parameter calibration and real-time correction using the Rainfall-Runoff-Inundation (RRI) model.
  • To provide guidance for the generalizable deployment of LLMs in environmental modeling.

Main Methods:

  • Development of two LLM integration frameworks: Copilot (human-AI collaboration) and Autopilot (LLM-driven automation).
  • Application of frameworks to the Rainfall-Runoff-Inundation (RRI) model in Japan's Kuzuryu River basin.
  • Evaluation of performance in parameter calibration and real-time correction tasks, utilizing prompt engineering and physics constraints.

Main Results:

  • The Copilot framework demonstrated robust performance, achieving high accuracy in parameter calibration (NSE 0.91/0.81) and stable real-time correction.
  • The Autopilot framework showed competence in physics-constrained calibration but failed in long-sequence real-time correction due to "attention decay".
  • LLMs are effective as knowledge engines and coding assistants under human supervision (Copilot), but full automation (Autopilot) is limited by context window constraints.

Conclusions:

  • Human-AI collaborative frameworks (Copilot) are currently more effective for complex environmental modeling tasks than fully automated ones (Autopilot).
  • Strategic task design, human oversight, and addressing LLM limitations like "attention decay" are crucial for successful LLM integration.
  • The study offers a methodological framework and design principles for deploying LLMs in environmental modeling, highlighting future research directions.