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

• 批准号: 1838024
• 负责人: Lei Yang
• 金额: $ 68.47万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 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=1838024&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Protecting

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.

该项目的目标是开发一个框架来实现实时烟雾输送预测和空气质量预测。 野火烟雾可以非常快速地传播，并对社区造成严重的健康危害。 由于输入数据的时空分辨率限制以及模拟大气条件所需的巨大计算能力，最先进的烟雾预报模型通常更新不频繁，并且提供粗空间分辨率的预测。 该项目将开发具有更好空间分辨率的实时烟雾传输和空气质量预测方法，以提高底层数据处理系统的可扩展性和效率，从而实现及时的空气质量警报。 虽然该项目应用于烟雾传输和空气质量预测，但这项工作可以推广到解决需要此类设计的许多其他大数据问题。主要研究人员将使用该项目的材料和主题，通过创建新的大数据分析相关课程并在内华达大学里诺分校开发大数据辅修课程来加强教育。 该项目还将提供机会，通过 K-12 推广和指导本科生和研究生，吸引更多来自代表性不足群体的学生，并影响一些学生的教育。这项研究的智力价值在于建立一种新颖的大数据驱动的空气质量预测针对野火烟雾提供及时有效的健康警报。计划中的新预测方法将把基于新颖的高斯马尔可夫随机场的实时时空预测与基于统计的长期时空预测相结合。为了解决高分辨率数据丢失的挑战，计划采用一种数据融合方法，将从摄像头网络收集的细粒度图像数据与空气污染监测数据相集成，以提高数据分辨率。基于深度神经网络的烟雾密度检测过程将从摄像机图像数据中提取空气质量信息。计划中的基于签名时间序列的新型预测方法将为使用有限的资源处理大量时空数据提供机会。通过根据时空属性识别关键数据，该项目将开发一个通信框架，以实现高效的相机数据传输。高效的并行和分布式数据处理对于支持实时处理大规模数据至关重要。计划中的基于分解的并行化方法和性能模型驱动的调度框架将实现高效的动态计算资源管理，这是该项目成功的关键。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

HDK: Toward High-Performance Deep-Learning-Based Kirchhoff Analysis

HDK：迈向基于深度学习的高性能基尔霍夫分析

• 发表时间: 2019-12
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Wang, Xinying;Tawose, Timothy;Yan, Feng;Zhao, Dongfang
• 通讯作者: Zhao, Dongfang

Using Novel Machine Learning Algorithms to Improve the Spatiotemporal Coverage of Satellite Aerosol Optical Depth

使用新颖的机器学习算法提高卫星气溶胶光学深度的时空覆盖范围

• 发表时间: 2019-12
• 期刊: AGU Fall Meeting 2019 (AGU 2019
• 作者: Huang, Jingting;Ghasemkhani, Amir;Marcela, Sandra;Salazar, Loria;Yan, Feng;Yang, Lei;Smirni, Evgenia;Redemann, Jens;Holmes, Heather
• 通讯作者: Holmes, Heather

SEFEE: lightweight storage error forecasting in large-scale enterprise storage systems

SEFEE：大型企业存储系统中的轻量级存储错误预测

• DOI: 10.5555/3433701.3433786
• 发表时间: 2020-11
• 期刊: Storage and Analysis (SC 2020
• 作者: Yazdi, Amirhesam;Lin, Xing;Yang, Lei;Yan, Feng
• 通讯作者: Yan, Feng

Not All Explorations Are Equal: Harnessing Heterogeneous Profiling Cost for Efficient MLaaS Training

并非所有探索都是平等的：利用异构分析成本进行高效的 MLaaS 培训

• 发表时间: 2020-04
• 期刊: Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS 2020
• 作者: Yi, Jun;Wang, Wei;Li, Cheng;Yan, Feng
• 通讯作者: Yan, Feng

EPNAS: Efficient Progressive Neural Architecture Search

EPNAS：高效渐进神经架构搜索

• 发表时间: 2019-09
• 期刊: Proceedings of the 2019 30th British Machine Vision Conference (BMVC 2019
• 作者: Zhou, Yanqi;Wang, Peng;Arik, Sercan;Yu, Haonan;Zawad, Syed;Yan, Feng;Greg, Diamos
• 通讯作者: Greg, Diamos