Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was used, and meteorological data 7 days before the damage were used as independent variables. A deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), which are representative deep learning techniques, were employed for the model development. Each model was trained and tested 30 times to evaluate the predictive performance. As a result of evaluating the predicted performance, the DNN-based model and the CNN-based model showed good performance, and the RNN-based model was analyzed to have relatively low performance. For the DNN-based model, the convergence epoch of the training showed a relatively wide distribution, which may lead to difficulties in selecting an epoch suitable for practical use. Therefore, the CNN-based model would be acceptable for the heavy rain damage prediction in terms of the accuracy and robustness. These results demonstrated the applicability of deep learning in the development of the damage prediction model. The proposed prediction model can be used for disaster management as the basic data for decision making.

中文翻译：

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使用深度学习预测暴雨灾害

使用深度学习技术开发了暴雨破坏预测模型，用于在暴雨破坏发生之前预测对区域的破坏。作为因变量，使用了由三类（轻微、显着、严重）组成的损害等级，并使用了损害前7天的气象数据作为自变量。模型开发采用了深度神经网络 (DNN)、卷积神经网络 (CNN) 和循环神经网络 (RNN)，它们是代表性的深度学习技术。每个模型都经过 30 次训练和测试，以评估预测性能。评估预测性能的结果，基于DNN的模型和基于CNN的模型表现出良好的性能，而基于RNN的模型被分析为性能相对较低。对于基于 DNN 的模型，训练的收敛epoch分布比较广，这可能导致难以选择适合实际使用的epoch。因此，就准确性和鲁棒性而言，基于 CNN 的模型对于大雨破坏预测是可以接受的。这些结果证明了深度学习在损伤预测模型开发中的适用性。提出的预测模型可作为灾害管理决策的基础数据。这些结果证明了深度学习在损伤预测模型开发中的适用性。提出的预测模型可作为灾害管理决策的基础数据。这些结果证明了深度学习在损伤预测模型开发中的适用性。提出的预测模型可作为灾害管理决策的基础数据。