Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

573
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
573
Typical Model Studies01:30

Typical Model Studies

602
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
602
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

266
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
266
Models, Theories, and Laws01:16

Models, Theories, and Laws

8.2K
Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...
8.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

255
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
255
Language and Cognition01:27

Language and Cognition

681
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
681

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Large language models: Tools for new environmental decision-making.

Journal of environmental management·2025
Same author

An Electrochemical Sensor Based on Electropolymerization of β-Cyclodextrin on Glassy Carbon Electrode for the Determination of Fenitrothion.

Sensors (Basel, Switzerland)·2023
Same author

Correlating microbial community compositions with environmental factors in activated sludge from four full-scale municipal wastewater treatment plants in Shanghai, China.

Applied microbiology and biotechnology·2016
Same author

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

Scientific reports·2016
Same author

Cardiotrophin-1 promotes cardiomyocyte differentiation from mouse induced pluripotent stem cells via JAK2/STAT3/Pim-1 signaling pathway.

Journal of geriatric cardiology : JGC·2016
Same author

Potential role of dipeptidyl peptidase-4 inhibitors in atrial fibrillation.

International journal of cardiology·2016

Related Experiment Video

Updated: Jan 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

983

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

Qiyang Nie1, Tong Liu2

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

Journal of Environmental Management
|December 31, 2025
PubMed
Summary

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.

Keywords:
Autopilot frameworkCalibrationCopilot frameworkEnvironmental modelingFlood modelLarge language modelReal-time correction

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

637
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Related Experiment Videos

Last Updated: Jan 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

983
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

637
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

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.