Hydro-ML: Symposium on Big Data Machine Learning in Hydrology and Water Resources; Pennsylvania, May 25-29, 2020

Hydro-ML：水文水资源大数据机器学习研讨会；

• 批准号: 2015680
• 负责人: Chaopeng Shen
• 金额: $ 4.87万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015680&HistoricalAwards=false
• 关键词: Hydro; ML; Symposium; Big; Data

Artificial intelligence has the potential to impact every facet of our society. Machine learning, an important component of artificial intelligence, is revolutionizing much of what we do today. Deep learning is a relatively new subset of machine learning that has tremendous potential to improve our capabilities for industrial applications and scientific discovery. The use of artificial intelligence in hydrology, and deep learning in particular, can bring tremendous benefits to society. Hydrologic academics and professionals, however, have not historically taken advantage of artificial intelligence. While there have been some workshops and gathering opportunities for hydrologists interested in deep learning, there have been no dedicated workshops to build a community that can leverage common datasets, methods, and goals. A Machine Learning in Water Workshop (“Hydro-ML”) is proposed to demystify artificial intelligence for a wider audience in hydrology, build dedicated expertise and collaborative potential through training sessions and hackathons. Building a hydrological machine learning community will enable sharing of resources, organization of competitions stimulating an expansion of the field, and encourage collaboration among academics and professionals to solve large challenges. Special attention will be paid to participants' diversity both in terms of gender and race, which has previously been identified as an issue in artificial intelligence and hydrology. The topics covered by the symposium will be defined through solicitations to the community at large, with the intention of stimulating broader impacts in the hydrologic community. The Hydro-ML symposium will build a collaborative community focusing on machine learning in hydrology. It will consist of four types of sessions: research presentations, breakout discussion forums, deep learning tutorials and hackathons, and community-building activities. Specific sessions will discuss forward-looking, overarching questions that will be solicited from the participants prior to the meeting. These symposium activities will provide the foundation to build collective efforts in novel dataset identification, preparations for machine learning-in-hydrology competitions, and discussion of physically-informed machine learning. The symposium will be widely advertised through diverse channels, including hydrology newsletters and mailing lists, targeted communications to underrepresented, non-profit, and industry groups, and publications aimed at the general public. The organizing committee will encourage participation from diverse and underrepresented communities, and welcome those who are new to artificial intelligence. The outcomes from the symposium include collective publications serving as positional statements by the community (including journal papers and white papers), detailed plans for hydrologic machine learning competitions, and technical deep learning tutorials for audience with varied deep learning experience, ranging from newcomers to intermediate users. The symposium’s events will be aimed at building a diverse community that enables advancements in hydrology which will benefit society.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能有可能影响我们社会的方方面面。机器学习是人工智能的重要组成部分，正在彻底改变我们今天所做的许多事情。深度学习是机器学习的一个相对较新的子集，具有巨大的潜力来提高我们的工业应用和科学发现的能力。在水文学中使用人工智能，特别是深度学习，可以为社会带来巨大的利益。然而，水文学者和专业人士在历史上并没有利用人工智能。虽然已经为对深度学习感兴趣的水文学家举办了一些研讨会和聚会机会，但还没有专门的研讨会来建立一个可以利用通用数据集、方法和目标的社区。水资源机器学习研讨会（“Hydro-ML”）旨在为水文学领域的更广泛受众揭开人工智能的神秘面纱，通过培训课程和黑客马拉松建立专门的专业知识和合作潜力。建立一个水文机器学习社区将能够共享资源，组织竞赛，刺激该领域的扩展，并鼓励学者和专业人士之间的合作，以解决重大挑战。将特别关注参与者在性别和种族方面的多样性，这在以前被认为是人工智能和水文学的一个问题。专题讨论会所涵盖的主题将通过向整个社区征求意见来确定，目的是在水文界产生更广泛的影响。Hydro-ML研讨会将建立一个专注于水文学机器学习的协作社区。它将包括四种类型的会议：研究演示，分组讨论论坛，深度学习教程和黑客松，以及社区建设活动。具体的会议将讨论前瞻性的、总体性的问题，这些问题将在会前向与会者征求意见。 这些研讨会活动将为在新的数据集识别、水文学机器学习竞赛的准备以及物理信息机器学习的讨论方面建立集体努力提供基础。将通过各种渠道广泛宣传专题讨论会，包括水文通讯和邮寄名单，针对代表性不足的非营利和行业团体的有针对性的通信，以及针对公众的出版物。组委会将鼓励来自多元化和代表性不足的社区的参与，并欢迎那些新的人工智能。研讨会的成果包括作为社区立场声明的集体出版物（包括期刊论文和白色论文），水文机器学习竞赛的详细计划，以及针对具有不同深度学习经验的观众（从新手到中级用户）的技术深度学习教程。该研讨会的活动旨在建立一个多元化的社区，促进水文学的进步，造福社会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Differentiable modelling to unify machine learning and physical models for geosciences

• DOI: 10.1038/s43017-023-00450-9
• 发表时间: 2023-07
• 期刊: Nature Reviews Earth & Environment
• 影响因子: 42.1
• 作者: Chaopeng Shen;A. Appling;P. Gentine;Toshiyuki Bandai;H. Gupta;A. Tartakovsky;M. Baity-Jesi;F. Fenicia;Daniel Kifer;Li Li-Li;Xiaofeng Liu;Wei Ren;Y. Zheng;C. Harman;M. Clark;M. Farthing;D. Feng;Praveen Kumar;Doaa Aboelyazeed;F. Rahmani;Yalan Song;H. Beck;Tadd Bindas;D. Dwivedi;K. Fang;Marvin Höge;Christopher Rackauckas;B. Mohanty;Tirthankar Roy;Chonggang Xu;K. Lawson