Data Analytics for Environmental Science and Engineering Research.
Environ Sci Technol
; 55(16): 10895-10907, 2021 08 17.
Article
em En
| MEDLINE
| ID: mdl-34338518
ABSTRACT
The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ciência de Dados
/
Ciência Ambiental
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article