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巨大破坏。风暴足迹（三天内最大阵风速度的地图）是与保险业和基础设施提供商密切相关的危险的重要总结。风暴足迹通常是根据气象数据和数值天气模型分析来估计的。然而，有一些有趣的不太结构化的数据源可以有助于估计风暴足迹，更重要的是可以提高我们估计的空间分辨率。这一点很重要，因为目前的方法还不能很好地解决一些重要的小规模气象现象，例如刺急流。我们建议利用三个额外的数据源（以及项目过程中可能的其他数据源）。迄今为止确定的三个来源是通过英国气象局天气观测网站（WOW）获得的业余观测、社交媒体上的评论以及社交媒体或闭路电视上录制的视频。业余气象观测目前由英国气象局收集，但不用于生成足迹估计。我们将研究是否可以使用它们来估计风暴足迹；一个有用的副产品是对每个 WOW 站的不确定性的估计。 Twitter 或 Instagram 等社交媒体经常包含有关风暴的评论。这些内容包括对风力大小的评论，以及风暴造成的损害的报告。在某些情况下，消息的地理位置由设备提供，但在其他情况下，必须推断出来。社交媒体上每天都会发布大量消息，应该可以使用这些消息来提供更详细的足迹模型。除了文本之外，社交媒体还记录图像和视频。视频也以闭路电视的形式广泛录制。例如，树木在风中吹动的视频记录包含有关风暴强度的信息。我们将分析这些记录以产生有关风速和阵风速度的信息。将大量不同的数据汇集在一起​​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。这两种方法都旨在将所有数据综合成风暴足迹（及其相关的不确定性）的估计值。前者将专注于生成更像当前基于最大阵风速度的估计的地图，而后者基于数据的方法将更多地专注于绘制风暴造成的损害。一旦我们通过社交媒体和建模对风暴足迹进行了估计，我们就会将其与标准产品进行比较，并与利益相关者协商，确定任何改进措施。

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.

关于气候变化的在线信息社交共享的意识形态偏见。

• 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

Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018

复杂网络及其应用 VII - 第 1 卷论文集第七届复杂网络及其应用国际会议 COMPLEX NETWORKS 2018

• DOI: 10.1007/978-3-030-05411-3_31
• 发表时间: 2019
• 作者: Bishop A

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