Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 55, Issue 19, Pages 12741-12754Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.1c01339
Keywords
applicability domain; artificial intelligence; best practices; feature importance; machine learning modeling; model applications; model interpretation; predictive modeling
Categories
Funding
- National Science Foundation [CBET-1804708]
- Laboratory Directed Research and Development (LDRD) program of the Los Alamos National Laboratory (LANL) [20190001DR]
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The article discusses the potential of machine learning to revolutionize data analysis in the field of environmental science and engineering, covering applications and considerations.
The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.
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