A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Environmental data is growing exponentially in quantity and complexity.
• High-quality big data analysis is essential for understanding environmental pollution.
• Machine learning (ML) offers powerful capabilities for analyzing complex environmental data.

Purpose of the Study:

• To introduce a novel research paradigm integrating ChatGPT with ML for environmental sustainability.
• To lower the barrier for environmental researchers in applying ML models.
• To address the knowledge gap in ML application within environmental science.

Main Methods:

• Leveraging ChatGPT to guide users through ML model application steps.
• Demonstrating ChatGPT's role in data preparation, model selection, construction, training, evaluation, and hyper-parameter optimization.
• Discussing the "ChatGPT + ML + Environment" research paradigm.

Main Results:

• The "ChatGPT + ML + Environment" paradigm simplifies the application of ML models for environmental research.
• ChatGPT can assist environmental researchers in all stages of ML model development.
• Challenges and limitations of this integrated approach are identified.

Conclusions:

• The "ChatGPT + ML + Environment" paradigm offers a promising approach to democratize ML in environmental sustainability.
• Further research into "secondary training" is crucial for optimizing this paradigm.
• This integration can accelerate advancements in environmental pollution characterization and management.