  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 increase in density and extent. Here, we use
a zero-inflated modeling approach and artificial neural networks to predict
tornado-induced property damage using publicly available data. We developed a
neural network that predicts whether a tornado will cause property damage
(out-of-sample accuracy = 0.821 and area under the receiver operating
characteristic curve, AUROC, = 0.872). Conditional on a tornado causing damage,
another neural network predicts the amount of damage (out-of-sample mean
squared error = 0.0918 and R2 = 0.432). When used together, these two models
function as a zero-inflated log-normal regression with hidden layers. From the
best-performing models, we provide static and interactive gridded maps of
monthly predicted probabilities of damage and property damages for the year
2019. Two primary weaknesses include (1) model fitting requires log-scale data
which leads to large natural-scale residuals and (2) beginning tornado
coordinates were utilized rather than tornado paths. Ultimately, this is the
first known study to directly model tornado-induced property damages, and all
data, code, and tools are publicly available. The predictive capacity of this
model along with an interactive interface may provide an opportunity for
science-informed tornado disaster planning.
