题目：Machine learning for Environmental Science: Opportunities, Research Overview, Insights, and Emerging Directions
报告人：皋磊
地点：2号学院楼2202
时间：2026年4月22日14:00
报告人简介
皋磊研究员，目前是澳大利亚联邦科学与工业研究组织（CSIRO）首席研究科学家，西澳大学和迪肯大学外聘博士生导师，同时担任国际应用系统分析研究所（IIASA）客座研究员。主要从事地球-人耦合系统建模与仿真、可持续发展政策评估、自然资源管理、机器学习、鲁棒决策等领域的基础与应用研究。皋研究员已发表论文200余篇，包括2篇《自然》正刊论文、3篇《自然-食品》、《自然-水》和《自然-城市》期刊论文、3篇《自然-通讯》期刊论文、2篇《美国国家科学院院刊》论文、1篇《柳叶刀行星健康》论文、5篇《One Earth》期刊论文，以及4篇《科学通报》期刊论文。多篇论文被评为“ESI热点论文”和“高被引论文”。
皋研究员曾荣获多个奖项，包括大洋洲建模与仿真学会杰出青年科学家奖、CSIRO农业部长奖、CSIRO职业生涯奖（表彰在CSIRO表现杰出的中青年科学家）、CSIRO“卓越科学”主席奖章以及CSIRO土地与水资源研究所最具影响力论文奖。此外，他还获得了两届国际人工智能会议的最佳论文奖，以及《细胞》出版社中国年度最佳论文奖（可持续发展类）。
报告摘要
Machine learning (ML) has emerged as a transformative paradigm in environmental science, enabling advanced data-driven understanding of complex Earth systems. With the rapid growth of multi-source environmental data—including satellite remote sensing, in-situ sensor networks, and climate model outputs—ML methods provide powerful tools for pattern recognition, prediction, and decision support. This overview highlights key opportunities where ML is reshaping environmental research, such as climate change modeling, air and water quality assessment, ecosystem monitoring, and extreme event prediction.
Recent advances demonstrate that deep learning and hybrid physics-informed models can significantly improve the accuracy and efficiency of environmental simulations compared to traditional approaches. At the same time, ML enables real-time environmental monitoring and supports adaptive management strategies under uncertainty. However, challenges remain, including data heterogeneity, limited interpretability, and the need for generalizable models across regions and scales.
Emerging directions point toward integrating domain knowledge with data-driven learning, developing explainable and trustworthy AI systems, and leveraging foundation models for Earth system science. Furthermore, coupling ML with digital twins of environmental systems is expected to enhance predictive capabilities and scenario analysis.