Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
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DOI：
10.1021/acs.est.1c01339
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发表时间：
2021-08-17
影响因子：
11.4
通讯作者：
Zhang, Huichun
中科院分区：
文献类型：
--
作者：
Zhong, Shifa;Zhang, Kai;Zhang, Huichun
环境科学与工程（ESE）领域每天生成的数据数量和复杂性都在迅速增加，这就要求数据分析技术不断进步。先进的数据分析方法，如机器学习（ML），已经成为揭示隐藏模式或推断相关性的不可或缺的工具，而传统的分析方法在这些方面面临着局限性或挑战。然而，ML的概念和实践还没有被ESE的研究人员广泛利用。该功能探索了ML在ESE领域彻底改变数据分析和建模的潜力，并涵盖了此类应用程序所需的基本知识。首先，我们使用五个例子来说明ML如何解决复杂的ESE问题。然后，我们总结了ML在ESE中的四种主要应用：进行预测;提取特征重要性;检测异常;以及发现新材料或化学品。接下来，我们将介绍ESE中ML应用程序所需的基本知识和当前的缺点，重点是应用ML时三个重要但经常被忽视的组件：正确的模型开发，正确的模型解释和合理的适用性分析。最后，我们讨论了ML工具在ESE应用中的挑战和未来的机遇，以突出ML在该领域的潜力。
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.