Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Jun Jie Zhu, Meiqi Yang, Zhiyong Jason Ren

Research output: Contribution to journalReview articlepeer-review

58 Scopus citations


Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Original languageEnglish (US)
Pages (from-to)17671-17689
Number of pages19
JournalEnvironmental Science and Technology
Issue number46
StatePublished - Nov 21 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Environmental Chemistry


  • Machine learning
  • causality
  • data leakage
  • data preprocessing
  • environmental research
  • hyperparameter optimization
  • model explainability
  • supervised learning


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