BIG data methods for improving windstorm FOOTprint prediction (BigFoot)
改进风暴足迹预测的大数据方法(BigFoot)
基本信息
- 批准号:NE/P017436/1
- 负责人:
- 金额:$ 194.98万
- 依托单位:
- 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:
- 关键词:
项目摘要
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(3天内最大阵风速度的地图)是对与保险业和基础设施提供商密切相关的危险的重要总结。传统上,风暴足迹是根据气象数据和数值天气模式分析估计的。然而,有几个有趣的结构化程度较低的数据源可能有助于估计风暴足迹,更重要的是将提高我们估计的空间分辨率。这一点很重要,因为有一些重要的小尺度气象现象,如刺痛射流,目前还没有很好地解决目前的方法。我们建议利用三个额外的数据源(在项目过程中可能还有其他数据源)。目前确定的三个来源是通过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CCTV上录制的视频。业余气象观测目前由气象局收集,但不用于产生足迹估计。我们将研究是否可以使用它们来估计风暴足迹;一个有用的副产品将是每个WOW站的不确定性估计。社交媒体,如推特或Instagram,经常包含对风暴的评论。这些内容包括对风有多大的评论,以及风暴造成的损失的报告。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,它必须被推断。每天在社交媒体上发布的信息数量非常多,应该可以使用这些信息来提供更详细的足迹模型。除了文字,社交媒体还记录图像和视频。还以闭路电视的形式广泛录制视频。比如说,树木在风中摇曳的视频记录包含了风暴强度的信息。我们会分析这些纪录,以提供有关风速和阵风速度的资料。汇集大量不同的数据是一个复杂的过程。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。这两种方法的目标都是将所有数据综合成风暴足迹的估计值(及其相关的不确定性)。前者将专注于根据最大阵风速度生成更像当前估计的地图,而后者基于数据的方法将更专注于绘制风暴造成的损失。一旦我们从社交媒体和建模中获得了风暴足迹的估计值,我们将把它们与标准产品进行比较,并与利益相关者协商,确定任何改进。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 作者:Tinkle Chugh;T. Krátký;K. Miettinen;Yaochu Jin;P. Makkonen
- 通讯作者: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:
- 影响因子:0
- 作者:Tinkle Chugh
- 通讯作者: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:
- 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
- 期刊:
- 影响因子:0
- 作者:Bishop A
- 通讯作者:Bishop A
Social sensing of floods in the UK.
- DOI:10.1371/journal.pone.0189327
- 发表时间:2018
- 期刊:
- 影响因子:3.7
- 作者:Arthur R;Boulton CA;Shotton H;Williams HTP
- 通讯作者:Williams HTP
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Peter Challenor其他文献
Propagating moments in probabilistic graphical models with polynomial regression forms for decision support systems
用于决策支持系统的具有多项式回归形式的概率图模型中的传播矩
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
V. Volodina;Nikki Sonenberg;Peter Challenor;Jim Q. Smith - 通讯作者:
Jim Q. Smith
Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo
量化印度尼西亚婆罗洲干旱和火灾风险的因果遥相关
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Timothy Lam;J. Catto;Rosa Barciela;A. Harper;Peter Challenor;Alberto Arribas - 通讯作者:
Alberto Arribas
Peter Challenor的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Peter Challenor', 18)}}的其他基金
Uncertainty Quantification at the Exascale (EXA-UQ)
百亿亿级不确定性量化 (EXA-UQ)
- 批准号:
EP/W007886/1 - 财政年份:2021
- 资助金额:
$ 194.98万 - 项目类别:
Research Grant
CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas)
CAMPUS(结合自主观测和模型来预测和理解陆架海)
- 批准号:
NE/R006768/1 - 财政年份:2018
- 资助金额:
$ 194.98万 - 项目类别:
Research Grant
From Models To Decisions (M2D)
从模型到决策 (M2D)
- 批准号:
EP/P016774/1 - 财政年份:2017
- 资助金额:
$ 194.98万 - 项目类别:
Research Grant
Uncertainty, Probability, Models And Climate Change
不确定性、概率、模型和气候变化
- 批准号:
NE/D000777/1 - 财政年份:2006
- 资助金额:
$ 194.98万 - 项目类别:
Research Grant
相似国自然基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
- 批准号:
- 批准年份:2024
- 资助金额:万元
- 项目类别:合作创新研究团队
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
- 批准号:
- 批准年份:2024
- 资助金额:万元
- 项目类别:外国青年学者研究基金项目
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
- 批准号:
- 批准年份:2020
- 资助金额:40 万元
- 项目类别:
基于高频信息下高维波动率矩阵估计及应用
- 批准号:71901118
- 批准年份:2019
- 资助金额:18.0 万元
- 项目类别:青年科学基金项目
半参数空间自回归面板模型的有效估计与应用研究
- 批准号:71961011
- 批准年份:2019
- 资助金额:16.0 万元
- 项目类别:地区科学基金项目
高频数据波动率统计推断、预测与应用
- 批准号:71971118
- 批准年份:2019
- 资助金额:50.0 万元
- 项目类别:面上项目
基于个体分析的投影式非线性非负张量分解在高维非结构化数据模式分析中的研究
- 批准号:61502059
- 批准年份:2015
- 资助金额:19.0 万元
- 项目类别:青年科学基金项目
基于Linked Open Data的Web服务语义互操作关键技术
- 批准号:61373035
- 批准年份:2013
- 资助金额:77.0 万元
- 项目类别:面上项目
体数据表达与绘制的新方法研究
- 批准号:61170206
- 批准年份:2011
- 资助金额:55.0 万元
- 项目类别:面上项目
一类新Regime-Switching模型及其在金融建模中的应用研究
- 批准号:11061041
- 批准年份:2010
- 资助金额:24.0 万元
- 项目类别:地区科学基金项目
相似海外基金
Developing methods for Big Data capture in support of the Digital Twin for Investment Casting Shelling
开发大数据捕获方法以支持熔模铸造脱壳的数字孪生
- 批准号:
2889986 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Studentship
Developing and exploring methods to understand human-nature interactions in urban areas using new forms of big data
利用新形式的大数据开发和探索理解城市地区人与自然相互作用的方法
- 批准号:
ES/W012979/1 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Research Grant
Risk stratifying indeterminate pulmonary nodules with jointly learned features from longitudinal radiologic and clinical big data
利用纵向放射学和临床大数据共同学习的特征对不确定的肺结节进行风险分层
- 批准号:
10678264 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Accessing and Expanding Natural Products Chemical Diversity by Big-data Analysis and Biosynthetic Investigation
通过大数据分析和生物合成研究获取和扩大天然产物化学多样性
- 批准号:
10714466 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Big Data Analytics Emerging Scholar (e-Scholar) Program for Minority Students
少数民族学生大数据分析新兴学者(e-Scholar)计划
- 批准号:
10554786 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
SUD-t Map: A Big Data Digital Platform to Identify and Characterize SUD Treatment Opportunities to Address Health Disparities
SUD-t 地图:一个大数据数字平台,用于识别和描述 SUD 治疗机会,以解决健康差异
- 批准号:
10594828 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Tackling Big Data problems in biomedical sciences with extended similarity methods
使用扩展相似性方法解决生物医学科学中的大数据问题
- 批准号:
10713143 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
Leveraging complementary big data methods and patient intervention designs to optimize neural markers of adolescent cannabis use
利用互补的大数据方法和患者干预设计来优化青少年大麻使用的神经标记
- 批准号:
10739527 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别:
University of South Carolina Big Data Health Science Conference
南卡罗来纳大学大数据健康科学会议
- 批准号:
10751656 - 财政年份:2023
- 资助金额:
$ 194.98万 - 项目类别: