Machine Learning in Environmental Research: Common Pitfalls and Best Practices

• Published in: Environmental science & technology Vol. 57; no. 46; pp. 17671 - 17689
• Main Authors: Zhu, Jun-Jie, Yang, Meiqi, Ren, Zhiyong Jason
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 21.11.2023; American Chemical Society (ACS)
• Subjects: Best practice; Complex variables; Data analysis; Data processing; Engineering; Environmental research; Environmental Sciences & Ecology; Familiarity; Learning algorithms; Machine Learning; Optimization; Supervised learning; data preprocessing; model explainability; supervised learning; Machine learning; data leakage; environmental research; hyperparameter optimization; causality
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.3c00026

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.