A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Machine learning (ML) offers powerful tools for analyzing complex environmental data.
• Current ML applications in environmental science are often hindered by a lack of domain knowledge integration, data quality issues, and methodological challenges.
• These limitations can compromise the reliability and interpretability of ML models in environmental contexts.

Purpose of the Study:

• To provide practical guidance on integrating domain expertise into machine learning workflows for environmental applications.
• To outline a coherent workflow for environmentally meaningful ML model development, from preprocessing to interpretability.
• To identify and address common pitfalls that affect the credibility of ML findings in environmental research.

Main Methods:

• The tutorial emphasizes integrating domain knowledge throughout the ML pipeline.
• It outlines a structured workflow encompassing data preprocessing, model development, evaluation, and interpretability.
• Key challenges and recurring pitfalls in applying ML to environmental data are examined.

Main Results:

• Effective integration of domain knowledge enhances the scientific grounding of ML models.
• A systematic workflow improves the reliability and interpretability of ML-driven environmental insights.
• Understanding and mitigating common pitfalls leads to more credible and actionable ML findings.

Conclusions:

• This tutorial provides a foundation for researchers to use machine learning rigorously and effectively in environmental science.
• It promotes scientifically grounded, methodologically sound, and decision-making-aligned ML practices.
• By addressing practical challenges, the guidance aims to enhance the utility of ML for environmental decision-making.