Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China.
Environ Pollut. 2023 Oct 15;335:122358. doi: 10.1016/j.envpol.2023.122358. Epub 2023 Aug 9.
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
机器学习（ML）和深度学习（DL）在环境生态与健康（EEH）领域的数据分析（例如特征提取、聚类、分类、回归、图像识别和预测）和风险评估与管理方面具有卓越的优势。考虑到 EEH 中数据的快速增长和日益复杂，总结 ML 和 DL 在 EEH 中的最新进展和应用具有重要意义。本综述总结了 ML 和 DL 建模的基本过程和基本算法，并指出了 ML 和 DL 在 EEH 中的迫切需求。强调了环境生态与恢复、新污染物的环境归宿、化学暴露与风险、化学危害识别与控制等近期研究热点。ML 和 DL 在 EEH 中的各种应用展示了它们的多功能性和技术革命，并提出了一些挑战。进一步概述了 ML 和 DL 在 EEH 中的观点，以促进 ML 驱动的研究范式的创新性分析和培养。
