Pang-Ning Tan awarded NSF grant

Pang-Ning Tan, Professor of Computer Science and Engineering, and Lifeng Luo, Associate Professor in the Department of Geography have been awarded a grant from the National Science Foundation for their project entitled “Prediction and Characterization of Extreme Events in Spatio-Temporal Data”.

Abstract:

Extreme weather and climate events such as hurricanes, heat waves, and droughts are destructive natural forces with the potential to cause devastating losses in property and human lives. According to the National Center for Environmental Information (NCEI), there have been more than 40 weather and climate disasters in the United States since 2017 that cost over $1 billion each, incurring over $460 billion in total losses and more than 3500 deaths. Given the severity of their impact, accurate prediction of the magnitude, frequency, timing, and location of such extreme events are critical to provide timely information to the public and to minimize the risk for human casualties and property destruction, thus advancing the national health, prosperity and welfare. However, despite their importance, forecasting the extreme events from spatio-temporal data is still a great challenge as the events to be detected are often rare and hard to predict. Identifying the spatio-temporal drivers of the extreme events is also a challenge as the events typically involve complex, nonlinear interactions between the underlying natural and anthropogenic processes. Through development and use of machine learning algorithms, this project will contribute to the advances of science to better predict these extreme events. 

This project aims to develop novel algorithms for predicting and characterizing extreme events in large-scale spatio-temporal data. Specifically, the planned research combines statistical theories for extreme value distribution with deep learning to enable accurate prediction and characterization of the extreme events. To achieve this goal, the planned research centers around the following three key areas: (1) development of deep learning algorithms with extreme value theory for predicting and characterizing extreme events in time series forecasting problems, (2) development of convolutional methods for joint extreme event forecasting at multiple locations, and (3) development of extreme event prediction methods for spatial trajectory data. As proof of concept, the planned methods will be applied to a variety of environmental monitoring applications, including the prediction of extreme weather events such as heat waves, droughts, and hurricanes. 


(Date Posted: 2020-08-20)