BIGDATA: IA: Collaborative Research: Protecting Yourself from Wildfire Smoke: Big Data-Driven Adaptive Air Quality Prediction Methodologies

大数据：IA：协作研究：保护自己免受野火烟雾的侵害：大数据驱动的自适应空气质量预测方法

基本信息

批准号：
1838022
负责人：
Evgenia Smirni
金额：
$ 29.83万
依托单位：
College of William and Mary
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838022&HistoricalAwards=false
关键词：
BIGDATA IA Collaborative Research Protecting

项目摘要

The objective of this project is to develop a framework to achieve real-time smoke transport prediction and air quality forecasting. Wildfire smoke can transport very fast and pose significant health hazards to communities. State-of-the-art smoke forecasting models typically have infrequent updates and provide predictions with a coarse spatial resolution due to spatiotemporal resolution limitations of input data and the tremendous computational power required to simulate atmospheric conditions. This project will develop real-time smoke transport and air quality prediction methodologies with better spatial resolution for improving the scalability and efficiency of the underlying data processing system to enable timely air quality alerts. While this project is applied towards smoke transport and air quality prediction, this work can be generalized to solve many other big data problems that require such design. The principal investigators will use the materials and topics from this project to enhance education by creating new big data analytics related courses and developing a Big Data Minor program at the University of Nevada, Reno. The project will also provide opportunities to engage more students from underrepresented groups and impact the education of several students, via K-12 outreach and mentoring undergraduate and graduate students.The intellectual merit of this research is in establishing a novel big data driven air quality prediction for wildfire smoke to provide timely and effective health alerts. The planned new prediction methodology will integrate the novel Gaussian Markov Random Field based real-time spatiotemporal prediction with statistical-based long-term spatiotemporal prediction. To tackle the challenge of missing high-resolution data, a data fusion methodology is planned to integrate fine-grained image data collected from camera networks with air pollution monitoring data to increase data resolution. A Deep Neural Network based smoke density detection process will extract air quality information from camera image data. The planned novel signature time-series based prediction methodology will open opportunities to process larger amounts of spatiotemporal data using limited resources. By identifying critical data based on spatiotemporal properties, the project will develop a communication framework that enables efficient camera data transfer. Efficient parallel and distributed data processing is utterly important to support processing large scale data in real time. The planned decomposition-based parallelization methodology and a performance model driven scheduling framework will enable efficient dynamic computing resource management, which is key to the success of this project.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.

该项目的目的是开发一个框架，以实现实时烟雾运输预测和空气质量预测。野火烟雾可以很快运输，并对社区造成严重的健康危害。最先进的烟雾预测模型通常会有很少的更新，并且由于空间分辨率的限制以及模拟大气条件所需的巨大计算能力，因此提供了粗糙的空间分辨率的预测。该项目将开发实时的烟雾传输和空气质量预测方法，并提供更好的空间分辨率，以提高基础数据处理系统的可扩展性和效率，以实现及时的空气质量警报。虽然该项目用于烟雾运输和空气质量预测，但可以将此工作推广以解决许多其他需要此类设计的大数据问题。主要研究人员将使用该项目的材料和主题来通过创建新的大数据分析课程并在内华达大学里诺大学开发大数据次要课程来增强教育。该项目还将通过K-12外展和指导的本科生和研究生提供机会吸引更多代表性群体的学生吸引更多的学生，并影响几个学生的教育。这项研究的知识分子在于建立一个新颖的大数据驱动空气质量预测，以预测野火烟雾，以提供及时和有效的健康警报。计划的新预测方法将将基于统计的长期时空预测与基于统计的长期时空预测相结合。为了应对缺少高分辨率数据的挑战，计划将数据融合方法与空气污染监测数据集成了从相机网络收集的细粒图像数据以增加数据分辨率。深度神经网络的烟雾密度检测过程将从相机图像数据中提取空气质量信息。计划中的新型签名时间序列方法将使用有限的资源打开机会，以处理大量时空数据。通过基于时空属性识别关键数据，该项目将开发一个通信框架，以实现有效的相机数据传输。有效的并行和分布式数据处理对于实时支持大规模数据非常重要。计划的基于分解的并行化方法和绩效模型驱动的调度框架将使有效的动态计算资源管理能够实现该项目成功的关键。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BATCH: Machine Learning Inference Serving on Serverless Platforms with Adaptive Batching

DOI：
10.1109/sc41405.2020.00073
发表时间：
2020-11
期刊：
SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Ahsan Ali;Riccardo Pinciroli;Feng Yan;E. Smirni
通讯作者：
Ahsan Ali;Riccardo Pinciroli;Feng Yan;E. Smirni

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