Machine Learning: New Ideas and Tools in Environmental Science and Engineering

TLDR

Environmental science and engineering face a rapid rise in complex data, making machine learning essential for uncovering hidden patterns, yet its adoption remains limited among researchers. This feature explores how machine learning can transform data analysis and modeling in environmental science and engineering, outlining the essential knowledge for such applications. The authors illustrate ML’s role through five case studies, categorize its four main applications—prediction, feature importance, anomaly detection, and material discovery—and highlight key gaps in model development, interpretation, and applicability. The article concludes by outlining challenges and future opportunities, underscoring ML’s transformative potential in environmental science and engineering.

Abstract

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.