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Jun‐Jie Zhu, Meiqi Yang, Zhiyong Jason Ren
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.
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Zhu, J., Yang, M., & Ren, Z. J. (2023). Machine Learning in Environmental Research: Common Pitfalls and Best Practices. Environmental Science & Technology, 57(46), 17671–17689. https://doi.org/10.1021/acs.est.3c00026
Zhu, Jun‐Jie, et al. "Machine Learning in Environmental Research: Common Pitfalls and Best Practices." Environmental Science & Technology, vol. 57, no. 46, 2023, pp. 17671–17689, https://doi.org/10.1021/acs.est.3c00026.
Zhu, Jun‐Jie, Meiqi Yang, and Zhiyong Jason Ren. "Machine Learning in Environmental Research: Common Pitfalls and Best Practices." Environmental Science & Technology 57, no. 46 (2023): 17671–17689. https://doi.org/10.1021/acs.est.3c00026.
Zhu J., Yang M., Ren Z. J. Machine Learning in Environmental Research: Common Pitfalls and Best Practices // Environmental Science & Technology. — 2023. — Vol. 57, № 46. — P. 17671–17689. — URL: https://doi.org/10.1021/acs.est.3c00026
@article{zhu2023machine,
title = {{Machine Learning in Environmental Research: Common Pitfalls and Best Practices}},
author = {Jun‐Jie Zhu and Meiqi Yang and Zhiyong Jason Ren},
journal = {Environmental Science \& Technology},
year = {2023},
volume = {57},
number = {46},
pages = {17671--17689},
doi = {10.1021/acs.est.3c00026},
url = {https://doi.org/10.1021/acs.est.3c00026}
}TY - JOUR AU - Zhu, Jun‐Jie AU - Yang, Meiqi AU - Ren, Zhiyong Jason TI - Machine Learning in Environmental Research: Common Pitfalls and Best Practices JO - Environmental Science & Technology VL - 57 IS - 46 SP - 17671 EP - 17689 PY - 2023 DA - 2023/06/29 DO - 10.1021/acs.est.3c00026 UR - https://doi.org/10.1021/acs.est.3c00026 ER -
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