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 $R^2$ = 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.

龙卷风是所有大气风暴中最猛烈的一次。在典型的一年中，美国经历了数百次龙卷风，相关损失高达十亿美元。对这些经济损失的准确预测将有助于社区的准备和复原力，特别是在龙卷风易发地区人口密度和范围增加的情况下。在这里，我们使用零膨胀建模方法和人工神经网络使用公开数据预测龙卷风引起的财产损失。我们开发了一种神经网络，可以预测龙卷风是否会造成财产损失（样本外准确度= 0.821，接收器工作特性曲线下的面积AUROC = 0.872）。在龙卷风造成损害的条件下，另一个神经网络可以预测损害的程度（样本外均方误差= 0。${\mathrm{[R}}^2$= 0.432）。当一起使用时，这两个模型可以用作具有隐藏层的零膨胀对数正态回归。从表现最好的模型中，我们提供了静态和交互式网格化地图，显示了2019年月度破坏和财产损失的每月预测概率。两个主要缺点包括：（1）模型拟合需要对数尺度的数据，从而导致大的自然尺度残差（2）利用了龙卷风的起始坐标，而不是龙卷风路径。最终，这是第一个直接模拟龙卷风引起的财产损失的已知研究，所有数据，代码和工具都是公开可用的。该模型的预测能力以及交互式界面可能为科学知悉的龙卷风灾难规划提供机会。