关于举办荷兰三角洲研究院/代尔夫特理工大学叶清华高级研究员学术讲座的通知
发布时间: 2022-10-20

题目:荷兰水智能:大数据、智能平台及应用

Dutch Water IntelligenceBig data, Intelligent platform and applications

时间:20221020日周四15001630

地点:腾讯会议 ID228828559

报告人:叶清华高级研究员(荷兰三角洲研究院/代尔夫特理工大学)

主持人:尹小玲副教授(水利工程系)


欢迎广大师生参加!

土木与交通学院

20221013



 

报告人简介:

叶清华,男,工学博士,现为荷兰三角洲研究院海岸河口动力地貌方向高级研究员\咨询专家,也是Delft理工大学土木工程和地球科学系兼职副教授,硕士与博士生导师。主要研究领域包括基于物理过程的河口、海岸和河流动力地貌数值模拟技术、生物动力地貌模型、浮泥和泥沙输移理论和水质模型、气候变化及适应对策以及海平面上升应对措施等等;开创性的把不同尺度动力地貌物理过程和生态动力学过程耦合在一起,奠定了生态动力地貌学数值模拟的理论框架;所开发的动力生态、动力地貌数学模型及其理论成果已被成功应用于世界各地的生态恢复、生态河口整治、滨海地带湿地恢复和生态工程研究和实践中,代表着生态动力地貌数值模拟技术的国际领先水平,是国际先进水动力-水质模型Delft3D模型的开发者之一。参与主持了多项荷兰和中国国家自然基金委重点国际(地区)合作研究项目,以及科技部国家重点研发计划政府间国际科技创新合作重点专项。 

Dr. Ye, Qinghua has broad experience in the fields of sediment transport, morphology, and numerical modelling, including physical process-based modelling, numerical methods and corresponding solvers, high performance computing (HPC). He recently worked on projects on Building with Nature, where he developed an integrated tool considering the interaction of wind, surface water, vegetation, wave, sediment transport and groundwater for wetland systems.

He is also one of the developers of the world-leading 3D/2D modelling suite, Delft3D for integral water solutions. His PhD research focused on an integrated geomorphologic model system to study the effect of ecology/biology change on geomorphology in salt marshes and wetlands.

After a MSc degree on coastal engineering and sediment transport, he was employed in the renowned Nanjing Hydraulic Research Institute, China. In 2012, Dr Ye completed his PhD in Civil Technology and Geoscience from the Delft University of Technology and UNESCO-IHE.

In addition to numerous technical reports, depicting the results of the various projects involved, Dr Ye has authored and/or co-authored many papers on mentioned subjects at international conferences and in technical journals.


报告摘要:

大数据到数据-知识的融合是我们一直致力的研究方向。通过机器学习和深度学习的技术方法,把我们的水、地下和基础设施的知识结合到日益增长和越来越广泛的数据中。Deltares研发了一个开放架构的智能大数据-模型融合平台Delft-FEWS提供给数据使用者、数据提供者和数据分析者。这个平台中采用的通用数据格式是数据交换、数据和模型融合的核心。

本报告将从以下三方面来开展介绍:

1.水相关大数据应用

2.数据应用和产生:下一代模型

3.智能大数据-模型融合平台 

For research and advice nationally and internationally, Deltares uses complex models and wide range of large data sets. We combine our system knowledge with data science to add value to our work and address advanced challenges related to water, subsurface and infrastructure. We use machine learning and deep learning to develop tools and models contributing in keeping delta areas around the world livability and safe. A data-modeling fusion platform Delft-FEWS was developed to link data and models in real time, producing forecasts on daily basis, to be used a country-wide national forecasting system using distributed client-server and cloud computing technology, and to be used at a much smaller scale on a simple desktop workstation, providing forecasts for a single basin. The foundation of Delft-FEWS is data-centric, with a common data-model through which all components interact. NetCDF with CF and UGRID format were used for data exchange.

In this presentation, we will demonstrate a few applications of big data firstly. Afterwards a brief introduction on the next generation numerical models as the kernel will be given, and in the end Delft-FEWS platform will be focused, as the data-model fusion platform.