Contact: Pang-Ning

Prediction and Characterization of Extreme Events in Spatio-Temporal Data

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. 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. However, forecasting the extreme events from spatio-temporal data is a great challenge as the events to be detected are 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. This project aims to develop novel algorithms for predicting and characterizing extreme events in large-scale spatio-temporal data. The proposed 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 for multiple locations? and (3) How to design a deep learning framework for extreme value prediction in spatial trajectory data.