Characterising the dynamics around offshore wind farms by combining machine learning and oceanographic techniques (4465)

通过结合机器学习和海洋学技术来表征海上风电场周围的动态 (4465)

• 批准号: 2784067
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2784067
• 关键词: Characterising; dynamics; around; offshore; wind

This project will use data science techniques and satellite data sets to better understand how offshore wind farms can impact the ocean. In particular, effects on the waves, water circulation and the movement of sediment can change the environment where wind farms are built and the oceans around them.Until recently, data have mainly been gathered using boats and fixed buoys. However, scientists increasingly use satellites and autonomous vehicles to collect offshore data. These technologies are providing new data sets and this project will aim to use data science techniques, including machine-learning, to combine new, exciting measurements with established techniques leveraging their respective strengths. The aim of this data fusion approach is to provide oceanographic data with resolution, accuracy and ease of access not previously obtained. These data sets will allow us to measure changes to the natural environment and identify the most suitable sites for offshore wind farms while protecting our oceans.The rapid growth in offshore wind combined with the importance of commercial and environmental viability of proposed projects mean that this project can make a valuable and timely difference to spatial planning and the sustainable management of marine resources.The project would suit someone with abilities or interest in any of the following fields, data science, the marine environment, offshore wind and remote sensing. The student will be based in the University of Exeter Penryn campus in Cornwall, South West UK. The project will include placements with 4 Earth Intelligence, also in Cornwall, where they will learn how the commercial data market operates for offshore projects alongside the commercial processes for purchasing and processing environmental satellite data. During the final year of research a further placement with 4EI will be used to analyse how the systems and architectures developed for physical oceanographic parameters can be integrated with commercial systems.

该项目将使用数据科学技术和卫星数据集，以更好地了解海上风电场如何影响海洋。特别是，对波浪、水循环和沉积物运动的影响可能会改变建造风力发电场的环境和周围的海洋。直到最近，数据主要是通过船只和固定浮标收集的。然而，科学家越来越多地使用卫星和自动驾驶工具来收集近海数据。这些技术正在提供新的数据集，该项目将致力于使用数据科学技术，包括机器学习，将新的、令人兴奋的测量与利用各自优势的现有技术相结合。这种数据融合方法的目的是为海洋数据提供以前从未获得的分辨率、准确性和易取性。这些数据集将使我们能够测量自然环境的变化，并在保护我们的海洋的同时确定最合适的海上风电场选址。海上风能的快速增长，加上拟议项目的商业和环境可行性的重要性，意味着该项目可以对空间规划和海洋资源的可持续管理产生宝贵和及时的影响。该项目将适合在以下任何领域有能力或感兴趣的人：数据科学、海洋环境、海上风能和遥感。这名学生将在英国西南部康沃尔的埃克塞特大学彭林分校学习。该项目将包括也在康沃尔的4 Earth Intelligence的实习，在那里他们将了解离岸项目的商业数据市场是如何运作的，以及购买和处理环境卫星数据的商业流程。在研究的最后一年，将进一步使用4EI来分析为物理海洋参数开发的系统和结构如何与商业系统相结合。