Abstract In South Korea, the risk of debris-flow is relatively high due to the country's vast mountainous topographical features and intense continuous rainfall during the summer. Debris-flows can result in the loss of human life and severe property damage, which can be made worse due to the poor spatiotemporal predictability of such hazards. Therefore, it is essential to research the preemptive prediction and mitigation of debris-flow hazards. For this purpose, this study developed an ANN model to predict the debris-flow volume based on 63 historical events. By considering the morphology, rainfall, and geology characteristics of the studied area in central South Korea, the data of 15 debris-flow predisposing factors were obtained. Among these data, four predisposing factors (watershed area, channel length, watershed relief, and rainfall data) were selected based on Pearson's correlation analysis to check for significant correlations with the debris-flow volume. To determine the best performing ANN model, a validation testing was carried out involving ten-fold cross-validation with MSE and R2 using both training and validation datasets, which were randomly split into a 7:3 ratio. The model performance validation results showed that an ANN model with two hidden neurons (4×2×1 architecture) had the highest R2 value (0.828) and the lowest MSE (0.022). In addition, in a comparative study with other existing regression models, the ANN model showed better results in terms of adjusted R2 value (0.911) using all datasets. Furthermore, 94% of the observed debris-flow volumes from the ANN model were within 1:2 and 2:1 lines of the predicted volumes. The results of this study have shown the potentiality of the developed ANN model to be a useful resource for decision-making and designing barriers in areas prone to debris-flows in South Korea.

中文翻译：

---

一种预测韩国中部地区极端降雨引起的泥石流体积的人工神经网络模型

摘要 韩国地势辽阔，夏季持续强降雨，发生泥石流的风险较高。碎石流可能导致人员伤亡和严重的财产损失，由于此类灾害的时空可预测性较差，情况可能会变得更糟。因此，研究泥石流灾害的先发制人预测和减缓是非常必要的。为此，本研究开发了一个人工神经网络模型来预测基于 63 个历史事件的泥石流体积。综合考虑韩国中部研究区的形态、降雨和地质特征，得到15个泥石流诱发因素数据。在这些数据中，四个诱发因素（流域面积、河道长度、流域地势、和降雨数据）是根据 Pearson 相关性分析选择的，以检查与泥石流体积的显着相关性。为了确定性能最佳的 ANN 模型，使用随机分成 7:3 的比例的训练和验证数据集对 MSE 和 R2 进行了十倍交叉验证的验​​证测试。模型性能验证结果表明，具有两个隐藏神经元（4×2×1 架构）的 ANN 模型具有最高的 R2 值（0.828）和最低的 MSE（0.022）。此外，在与其他现有回归模型的比较研究中，ANN 模型在使用所有数据集的调整后的 R2 值 (0.911) 方面显示出更好的结果。此外，从人工神经网络模型中观察到的泥石流体积的 94% 在预测体积的 1:2 和 2:1 线内。