BIG data methods for improving windstorm FOOTprint prediction (BigFoot)

改进风暴足迹预测的大数据方法（BigFoot）

• 批准号: NE/P017436/1
• 负责人: Peter Challenor
• 金额: $ 194.98万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FP017436%2F1
• 关键词: BIG; data; methods; improving; windstorm

Wind storms can cause great damage to property and infrastructure. The windstorm footprint (a map of maximum wind gust speed over 3 days) is an important summary of the hazard of great relevance to the insurance industry and to infrastructure providers. Windstorm footprints are conventionally estimated from meteorological data and numerical weather model analyses. However there are several interesting less structured data sources that could contribute to the estimation of the wind storm footprints, and more importantly will raise the spatial resolution of our estimates. This is important as there are important small-scale meteorological phenomena, such as sting jets, that are currently not well resolved by the current methods. We propose to exploit three additional sources of data (and possibly others during the course of the project). The three sources so far identified identified are amateur observations available through the Met Office weather observations website (WOW), comments made on social media and video recorded on social media or CCTV. Amateur meteorological observations are currently collected by the Met Office but not used in producing the footprint estimates. We will investigate whether we can use them in the estimation of the storm footprint; a useful by-product will be estimates of the uncertainty for each WOW station. Social media, such as twitter or instagram, often contains comments on windstorms. These can range from comments on how windy it is, to reports of damage produced by storms. In some cases the geographical location of the message is provided by the device but in others it has to be inferred. There are very large numbers of messages posted on social media every day and it should be possible to used these to provide more detailed modelling of footprints. In addition to text, social media also records images and video. Video is also recorded extensively in the form of CCTV. Video recordings of trees, say, blowing in the wind include information on the strength of the windstorm. We will analyse such recordings to produce information on wind velocity and gust velocity. Bringing together large quantities of diverse data is a complex procedure. We will develop, test, and compare two approaches in modern data science: statistical process modelling and machine learning. Both methods will aim to synthesise all the data into an estimate of the windstorm footprint (and its associated uncertainty). The former will concentrate on producing a map more like the current estimates based on the maximum gust speed while the latter data based methods will concentrate more on mapping the damage caused by the storm. Once we have estimates of the windstorm footprint from both social media and the modelling we will compare these with the standard products and, in consultation with stakeholder, establish any improvements.

风暴会对财产和基础设施造成极大的破坏。暴风雨足迹（在3天内的最大风阵速度图）是与保险业和基础设施提供商危害危险的重要摘要。风暴足迹通常是根据气象数据和数值天气模型分析来估算的。但是，有几种有趣的结构化数据源可能有助于对风风暴足迹的估计，更重要的是，将提高我们估计值的空间分辨率。这很重要，因为存在重要的小规模气象现象，例如sting喷气机，这些现象目前无法通过当前方法很好地解决。我们建议利用三个其他数据来源（在项目过程中可能是其他数据来源）。到目前为止确定的三个来源是通过MET Office Weather观察网站（WOW）获得的业余观察，在社交媒体上发表的评论以及在社交媒体或CCTV上记录的视频。当前，大都会办公室收集了业余气象观察，但没有用于产生足迹估计。我们将调查是否可以在风暴足迹的估计中使用它们；有用的副产品将是每个WOW站的不确定性估计。社交媒体，例如Twitter或Instagram，经常包含对暴风雨的评论。这些范围从关于风的评论到暴风雨造成的损害的报道。在某些情况下，该消息的地理位置由设备提供，但必须推断出该消息的地理位置。每天在社交媒体上发布大量消息，应该可以使用它们来提供更详细的足迹建模。除文本外，社交媒体还记录了图像和视频。视频还以CCTV的形式广泛记录。例如，在风中吹树的视频记录包括有关风暴强度的信息。我们将分析此类记录，以产生有关风速和阵风速度的信息。将大量不同数据汇总在一起是一个复杂的过程。我们将开发，测试和比较现代数据科学中的两种方法：统计过程建模和机器学习。两种方法都旨在将所有数据综合为风暴足迹（及其相关的不确定性）的估计。前者将集中精力根据最大阵风速度产生当前估计的地图，而后者基于数据的方法将更多地集中于映射暴风雨造成的损害。一旦我们估算了社交媒体和建模的暴风雨足迹，我们将与标准产品进行比较，并与利益相关者协商，建立任何改进。

项目成果

Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm

• DOI: 10.1145/3321707.3321745
• 发表时间: 2019-07
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Tinkle Chugh;T. Krátký;K. Miettinen;Yaochu Jin;P. Makkonen

Scalarizing Functions in Bayesian Multiobjective Optimization

• DOI: 10.1109/cec48606.2020.9185706
• 发表时间: 2019-04
• 期刊: 2020 IEEE Congress on Evolutionary Computation (CEC)
• 作者: Tinkle Chugh

Ideological biases in social sharing of online information about climate change.

• DOI: 10.1371/journal.pone.0250656
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Cann TJB;Weaver IS;Williams HTP
• 通讯作者: Williams HTP

Social sensing of floods in the UK.

• DOI: 10.1371/journal.pone.0189327
• 发表时间: 2018
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Arthur R;Boulton CA;Shotton H;Williams HTP
• 通讯作者: Williams HTP

Is it correct to project and detect? How weighting unipartite projections influences community detection

• DOI: 10.1017/nws.2020.11
• 发表时间: 2020-07-01
• 期刊: NETWORK SCIENCE
• 影响因子: 1.7
• 作者: Cann, Tristan J. B.;Weaver, Iain S.;Williams, Hywel T. P.
• 通讯作者: Williams, Hywel T. P.