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

该项目将使用数据科学技术和卫星数据集来更好地了解海上风力发电场如何影响海洋。特别是，对波浪、水循环和沉积物运动的影响可以改变风力发电场的建造环境和周围的海洋。直到最近，数据主要是通过船只和固定浮标收集的。然而，科学家们越来越多地使用卫星和自动驾驶汽车来收集海上数据。这些技术提供了新的数据集，该项目旨在使用包括机器学习在内的数据科学技术，将新的、令人兴奋的测量方法与现有技术结合起来，利用各自的优势。这种数据融合方法的目的是提供以前无法获得的分辨率、准确性和易获取性的海洋数据。这些数据集将使我们能够测量自然环境的变化，并在保护海洋的同时确定最合适的海上风力发电场地点。海上风电的快速增长，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做出有价值和及时的改变。该项目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。该学生将在英国西南部康沃尔郡的埃克塞特大学彭林校区工作。该项目将包括在康沃尔的4地球情报机构实习，在那里他们将学习商业数据市场如何为海上项目运作，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，将使用4EI的进一步安置来分析如何将为物理海洋学参数开发的系统和架构与商业系统集成。

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.