A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for deco...

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Vydáno v:Eco-Environment & Health Ročník 3; číslo 2; s. 131 - 136
Hlavní autoři: An, Haoyuan, Li, Xiangyu, Huang, Yuming, Wang, Weichao, Wu, Yuehan, Liu, Lin, Ling, Weibo, Li, Wei, Zhao, Hanzhu, Lu, Dawei, Liu, Qian, Jiang, Guibin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.06.2024
Elsevier
Témata:
ChatGPT
Environmental application
Machine learning
Secondary training
ChatGPT
Secondary training
Machine learning
Environmental application
ISSN:2772-9850, 2772-9850
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Shrnutí:The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm—“ChatGPT + ML + Environment”, providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of “secondary training” for future application of “ChatGPT + ML + Environment”. [Display omitted] •A new paradigm of “ChatGPT + Machine learning (ML) + Environment” is presented.•The novelty and knowledge gaps of ML for decoupling the complexity of environmental big data are discussed.•The new paradigm guided by GPT reduces the threshold of using Machine Learning in environmental research.•The importance of “secondary training” for using “ChatGPT + ML + Environment” in the future is highlighted.
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ISSN:2772-9850
2772-9850
DOI:10.1016/j.eehl.2024.01.006