I-Corps: Geospatial Trend Detection for Hydro-power and Critical Infrastructure Design

I-Corps:水电和关键基础设施设计的地理空间趋势检测

基本信息

  • 批准号:
    2344120
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-11-15 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

The broader impact/commercial potential of this I-Corps project is the development of a trend detection software tool to predict climate change risks related to water. Climate change risks manifest primarily through water, causing extremes (droughts and floods) and systematic shifts in the water systems’ time series. With robust trend detection tools, it may be possible to factor in climate change impacts when calculating the annual rate of change of loss and net economic returns from building large and expensive infrastructure projects, like dams. The proposed technology may identify the changing mean conditions of hydro-climatic (or other) variables that have a year-to-year variability, but on average could be shifting away from the historical mean behavior, called non-stationarity of mean. Some examples of infrastructure that may benefit from this technology include engineering design estimations of hydro-climate extreme return periods (e.g., droughts and floods) for pipe networks for water supply or wastewater, dams (hydro-power generation), and climate-impact on critical infrastructure (power grids, nuclear and mining sites). In addition, this technology may promote rapid adoption of climate resilient infrastructure design standards.This I-Corps project is based on the development of technology for robust trend detection, or “non-stationarity detection.” The proposed technology combines the results from several decades of independent literature in hydro-climate sciences and econometrics to achieve a step-change improvement in detecting non-stationary trends. In addition to geosciences, these developments are of fundamental importance in other applied science fields, including econometrics, biometrics, and psychometrics. Numerous variables in these domains are monitored for their changing behavior over time (trending behavior), but often their changes are also strongly correlated in time (predictably oscillation), which typically confounds the ability to detect a trending behavior. The proposed technology helps cut through this noise. In doing so, false negatives and false positives on non-stationarity detection were reduced by up to 30% and 400%, respectively. By using this information, operational and capital planning costs may be optimized by minimizing misplaced spending for climate resilient infrastructure, and maximizing spending where it is needed most.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
I-Corps项目更广泛的影响/商业潜力是开发一个趋势检测软件工具,以预测与水有关的气候变化风险。气候变化风险主要通过水表现出来,导致极端情况(干旱和洪水)和水系统时间序列的系统性变化。有了强大的趋势检测工具,在计算大坝等大型且昂贵的基础设施项目的年损失率和净经济回报时,可能会考虑气候变化的影响。拟议的技术可以识别具有年际变化的水文气候(或其他)变量的变化的平均条件,但平均可能偏离历史平均行为,称为平均的非平稳性。可能受益于这项技术的基础设施的一些例子包括供水或废水管网的水文气候极端重现期(例如干旱和洪水)的工程设计估计、大坝(水力发电)以及气候对关键基础设施(电网、核电站和矿场)的影响。此外,这项技术可能会促进气候适应性基础设施设计标准的快速采用。这个i-Corps项目是基于稳健趋势检测技术的开发,或称“非平稳检测”。拟议的技术结合了几十年来在水文气候科学和计量经济学方面的独立文献的结果,在检测非平稳趋势方面实现了阶跃变化的改进。除了地球科学,这些发展在其他应用科学领域也具有重要意义,包括计量经济学、生物计量学和心理计量学。这些领域中的许多变量被监控其随时间变化的行为(趋势行为),但通常它们的变化在时间上也是强相关的(可预测的振荡),这通常会混淆检测趋势行为的能力。这项拟议的技术有助于消除这种噪音。通过这样做,非平稳性检测的假阴性和假阳性分别减少了30%和400%。通过使用这些信息,运营和资本规划成本可以通过最大限度地减少错位的气候适应性基础设施支出,以及最需要的支出来优化。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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Anna Scaglione其他文献

Stochastic Dynamic Network Utility Maximization with Application to Disaster Response
随机动态网络效用最大化及其在灾难响应中的应用
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Anna Scaglione;Nurullah Karakoç
  • 通讯作者:
    Nurullah Karakoç
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA, March 23-27, 2020
2020 IEEE 国际普适计算和通信研讨会研讨会,PerCom Workshops 2020,美国德克萨斯州奥斯汀,2020 年 3 月 23-27 日
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yuan Lai;Gonzalo J. Martinez;Stephen M. Mattingly;Shayan Mirjafari;Subigya Nepal;Andrew T Campbell;A. Dey;Aaron D. Striegel;Marco Jansen;Fatjon Seraj;Wei Wang;P. Havinga;Kaijie Zhang;Zhiwen Yu;Dong Zhang;Zhu Wang;Bin Guo;Julian Graf;Katrin Neubauer;Sebastian Fischer;Rudolf Hackenberg;Elliott Wen;Gerald Weber;Javier Rojo;Daniel Flores;J. García;J. M. Murillo;Javier Berrocal;Mingyu Hou;Tianyu Kang;Li Guo;Edison Thomaz;Beichen Yang;Min Sun;Xiaoyan Hong;Xiaoming Guo;P. Barsocchi;A. Crivello;Michele Girolami;Fabio Mavilia;Vivek Chandel;Shivam Singhal;Avik Ghose;Tetsushi Matsuda;Toru Inada;Susumu Ishihara;Luay Alawneh;Belal Mohsen;Mohammad Al;Ahmed S. Shatnawi;Mahmoud Al;N. B. Rabah;Eoin Brophy;W. Muehlhausen;A. Smeaton;Tomás E. Ward;S. Maskey;S. Badsha;Shamik Sengupta;Ibrahim Khalil;Stanisław Saganowski;Anna Dutkowiak;A. Dziadek;Maciej Dziezyc;Joanna Komoszynska;Weronika Michalska;Adam G. Polak;Michal Ujma;Przemysław Kazienko;Nurullah Karakoç;Anna Scaglione;Fatemeh Mirzaei;Jonathan Lam;Roberto Manduchi;R. K. Ramakrishnan;R. Gavas;Lalit Venkata Subramaninan Viraraghavan;Kumar Hissaria;Arpan Pal;P. Balamuralidhar;S. Ditton;Ali Tekeoglu;K. Bekiroglu;Seshadhri Srinivasan;E. Tonkin;Miquel Perello Nieto;Haixia Bi;Antonis Vafeas;Yuri Tani;M. Garcia;A. Konios;M. A. Mustafa;C. Nugent;G. Morrison;Noah Sieck;Cameron Calpin;Mohammad S. Almalag;M. M. Sandhu;Kai Geissdoerfer;Sara Khalifa;Raja Jurdak;Marius Portmann;Brano Kusy;Alwyn Burger;Chao Qian;Gregor Schiele;Domenik Helms;Peter Zdankin;Marian Waltereit;V. Matkovic;Torben Weis;Syafiq Al Atiiq;Christian Gehrmann;Jae Woong Lee;Sumi Helal;Mathias Mormul;Christoph Stach;L. Krupp;G. Bahle;Agnes Gruenerbl;P. Lukowicz;Nicholas Handaja;Brent Lagesse;Clémentine Gritti;Dennis Przytarski;Bernhard Mitschang;Yeongjun Jeon;Kukho Heo;Soon Ju Kang;Sandeep Biplav Srivastava;Singh Sandha;Vaskar Raychoudhury;Sukanya Randhawa;V. Kapoor;Anmol Agrawal;Young D. Kwon;Kirill A. Shatilov;Lik;Serkan Kumyol;Kit;Yui;Pan Hui;Brittany Lewis;Joshua Hebert;Krishna Venkatasubramanian;Matthew Provost;Kelly Charlebois;Kristina Yordanova;Albert Hein;T. Kirste;Lien;Jun;Wei;Casper Van Gheluwe;I. Šemanjski;Suzanne Hendrikse;S. Gautama;Furqan Jameel;Zheng Chang;Riku Jäntti;Sergio Laso;M. Linaje;Ikram Ullah;N. Meratnia;Steven M. Hernandez;Eyuphan Bulut;Amiah Gooding;Matthew Martin;Maxwell Minard;Smruthi Sandhanam;Travis Stanger;Yana Alexandrova;Ashfaq Khokhar;Goce Trajcevski;Utsav Goswami;Kevin Wang;Gabriel Nguyen;Federico Montori;L. Bedogni;Gianluca Iselli;L. Bononi;Saptaparni Kumar;Haochen Pan;Roger Wang;Lewis Tseng;K. Hirayama;S. Saiki;Masahide Nakamura;Kiyoshi Yasuda;Samy El;Ismail Arai;Ahmad Salman;B. B. Park;Yuya Sano;Yuito Sugata;Teruhiro Mizumoto;H. Suwa;K. Yasumoto;P. Kouris;Marietta Sionti;Chrysovalantis Korfitis;Stella Markantonatou;Naima Khan;Nirmalya Roy;D. Jaiswal;D. Chatterjee;Ramesh Kumar;Ana Cristina Franco;Da Silva;Pascal Hirmer;Jan Schneider;Seda Ulusal;Matheus Tavares;Tomokazu Matsui;Kosei Onishi;Shinya Misaki;Manato Fujimoto;Hayata Satake;Yuki Kobayashi;Ryotaro Tani;Hiroshi Shigeno;Avijoy Chakma;Abu Zaher;Md Faridee;M Sajjad Hossain;Cleo Forman;Pablo Thiel;Raymond Ptucha;Miguel Dominguez;Cecilia Ovesdotter Alm;S. Mozgai;Arno Hartholt;Albert Rizzo
  • 通讯作者:
    Albert Rizzo
Network-Constrained Reinforcement Learning for Optimal EV Charging Control
用于最佳电动汽车充电控制的网络约束强化学习
Routing and data compression in sensor networks: stochastic models for sensor data that guarantee scalability

Anna Scaglione的其他文献

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{{ truncateString('Anna Scaglione', 18)}}的其他基金

Travel Grant: Urban Tech Academy meeting on electrified multimodal transportation
旅行补助金:城市技术学院关于电气化多式联运的会议
  • 批准号:
    2336001
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Advancing Graph Signal Processing Techniques for Monitoring and Control of Electric Distribution Power Systems
先进的图形信号处理技术用于配电电力系统的监测和控制
  • 批准号:
    2210012
  • 财政年份:
    2022
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
CCF-BSF: CIF: Small: Identification and Isolation of Malicious Behavior in Multi-Agent Optimization Algorithms
CCF-BSF:CIF:小:多代理优化算法中恶意行为的识别和隔离
  • 批准号:
    1714672
  • 财政年份:
    2017
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
EAGER: The Identification of Social Systems Trust: Theory and Experimental Validation
EAGER:社会系统信任的识别:理论与实验验证
  • 批准号:
    1553746
  • 财政年份:
    2015
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER: Renewables: A function space theory for continuous-time flexibility scheduling in electricity markets
合作研究:EAGER:可再生能源:电力市场连续时间灵活性调度的函数空间理论
  • 批准号:
    1549923
  • 财政年份:
    2015
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling
CCF:小型:采用亚奈奎斯特采样的射频频谱在线学习和利用
  • 批准号:
    1534957
  • 财政年份:
    2014
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks
CIF:大型:协作研究:认知网络上的合作与学习
  • 批准号:
    1531050
  • 财政年份:
    2014
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant
CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling
CCF:小型:采用亚奈奎斯特采样的射频频谱在线学习和利用
  • 批准号:
    1320065
  • 财政年份:
    2013
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks
CIF:大型:协作研究:认知网络上的合作与学习
  • 批准号:
    1011811
  • 财政年份:
    2010
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant
NeTS: Medium: Collaborative Research: Unlocking Capacity for Wireless Access Networks through Robust Cooperative Cross-Layer Design
NetS:媒介:协作研究:通过稳健的协作跨层设计释放无线接入网络的容量
  • 批准号:
    0905267
  • 财政年份:
    2009
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant

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