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. The planned research is transformative as it will shed light on the following key issues: (1) How to bridge the gap between current extreme value theory for modeling the tail distribution of random phenomena with deep learning. (2) How to design spatio-temporal deep learning approaches that can accurately forecast the magnitude, frequency, and timing of extreme events at multiple locations. and (3) How to design a deep learning framework for extreme value prediction in spatial trajectory data. Successful completion of this project will be a significant step forward towards resolving these issues.
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.
