Course Outline

Introduction to Environmental Modeling with LLMs

  • The role of AI in environmental science
  • Overview of LLMs and their capabilities in data analysis
  • Case studies: LLMs in climate and environmental research

LLMs for Data Analysis and Prediction

  • Preprocessing environmental data for LLMs
  • Building predictive models for weather and climate patterns
  • Assessing the impact of environmental policies with LLMs

LLMs in Conservation and Biodiversity

  • Modeling ecosystems and biodiversity with LLMs
  • LLMs for tracking and predicting species distribution
  • Using LLMs to support conservation planning

LLMs for Environmental Impact and Policy

  • Analyzing environmental impact reports with LLMs
  • LLMs in policy development and public communication
  • Engaging stakeholders with data-driven insights

Hands-on Lab: Environmental Project with LLMs

  • Developing an environmental model using LLMs
  • Simulating scenarios and analyzing outcomes
  • Presenting results to support environmental strategies

Summary and Next Steps

Requirements

  • An understanding of environmental science and data analysis
  • Experience with Python programming
  • Familiarity with statistical modeling and machine learning

Audience

  • Environmental scientists and researchers
  • Data analysts
  • Policy makers and environmental advocates
 14 Hours

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