Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series

Active volcanoes are globally distributed and pose societal risks at multiple geographic scales, ranging from local hazards to regional/international disruptions. Many volcanoes do not have continuous ground monitoring networks; meaning that satellite observations provide the only record of volcanic behavior and unrest. Among these remote sensing observations, thermal imagery is inspected daily by volcanic observatories for examining the early signs, onset, and evolution of eruptive activity. However, thermal scenes are often obstructed by clouds, meaning that forecasts must be made off image sequences whose scenes are only usable intermittently through time. Here, we explore forecasting this thermal data stream from a deep learning perspective using existing architectures that model sequences with varying spatiotemporal considerations. Additionally, we propose and evaluate new architectures that explicitly model intermittent image sequences. Using ASTER Kinetic Surface Temperature data for $9$ volcanoes between $1999$ and $2020$, we found that a proposed architecture (ConvLSTM + Time-LSTM + U-Net) forecasts volcanic temperature imagery with the lowest RMSE ($4.164^{\circ}$C, other methods: $4.217-5.291^{\circ}$C). Additionally, we examined performance on multiple time series derived from the thermal imagery and the effect of training with data from singular volcanoes. Ultimately, we found that models with the lowest RMSE on forecasting imagery did not possess the lowest RMSE on recreating time series derived from that imagery and that training with individual volcanoes generally worsened performance relative to a multi-volcano data set. This work highlights the potential of data-driven deep learning models for volcanic unrest forecasting while revealing the need for carefully constructed optimization targets.

Predicting property damage from tornadoes with deep learning

Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations in tornado-prone areas continue to increase in density and extent. Here, we use artificial neural networks to predict tornado-induced property damage using publicly available data. We find that the large number of tornadoes which cause zero property damage (30.6% of the data) poses a challenge for predictive models. We developed a model that predicts whether a tornado will cause property damage to a high degree of accuracy (out of sample accuracy = 0.829 and AUROC = 0.873). Conditional on a tornado causing damage, another model predicts the amount of damage. When combined, these two models yield an expected value for the amount of property damage caused by a tornado event. From the best-performing models (out of sample mean squared error = 0.089 and R2 = 0.473), we provide an interactive, gridded map of monthly expected values for the year 2018. One major weakness is that the model predictive power is optimized with log-transformed, mean-normalized property damages, however this leads to large natural-scale residuals for the most destructive tornadoes. The predictive capacity of this model along with an interactive interface may provide an opportunity for science-informed tornado disaster planning.