Maqola

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

Shifa ZhongDepartment of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United StatesKai ZhangDepartment of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United StatesMajid BagheriDepartment of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United StatesJoel G. BurkenDepartment of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United StatesApril Z. GuDepartment of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14850, United StatesBaikun LiDepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United StatesXingmao MaDepartment of Civil and Environmental Engineering, Texas A&M University, College Station, Texas, 77843, United StatesBabetta L. MarroneBioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesZhiyong Jason RenDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United StatesJoshua SchrierDepartment of Chemistry, Fordham University, The Bronx, New York 10458 United StatesWei ShiSchool of Environment, Nanjing University, Nanjing, 210093 ChinaHaoyue TanSchool of Environment, Nanjing University, Nanjing, 210093 ChinaTianbao WangDepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United StatesXu WangResearch Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaBryan M. WongDepartment of Chemical & Environmental Engineering, Materials Science & Engineering Program, University of California-Riverside, Riverside, California 92521 United StatesXusheng XiaoDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio 44106, United StatesXiong YuDepartment of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United StatesJun‐Jie ZhuDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United StatesHuichun ZhangDepartment of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States

2021en

ABI

Annotatsiya

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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Identifikatorlar

DOI: 10.1021/acs.est.1c01339

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