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

该项目的目标是建立一个框架，以实现实时烟雾输送预测和空气质量预测。野火烟雾传播速度非常快，对社区健康造成重大危害。由于输入数据的时空分辨率限制和模拟大气条件所需的巨大计算能力，最先进的烟雾预测模型通常更新频率不高，提供的预测空间分辨率很低。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，以提高基础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输送和空气质量预测，但这项工作可以推广到解决许多其他需要此类设计的大数据问题。主要研究人员将利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和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外展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了一种新的大数据驱动的野火烟雾空气质量预测，以提供及时有效的健康警报。计划中的新预测方法将基于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。为了解决缺少高分辨率数据的问题，研究人员计划采用一种数据融合方法，将从摄像机网络收集的细粒度图像数据与空气污染监测数据集成在一起，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预测方法将为使用有限资源处理更大量的时空数据提供机会。通过识别基于时空属性的关键数据，该项目将开发一种通信框架，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大规模数据的实时处理至关重要。基于计划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，这是本项目成功的关键。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.