Abstract

Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of Fuzzy Logic algorithm with the following four machine learning models: Classification and Regression Tree, Deep Learning Neural Network, XGBoost, and Naïve Bayes. Ten flash-flood predictors were used as independent variables to determine the flash-flood potential index. As a dependent variable, we used areas with ttorrential phenomena divided into training (70%) and validating data set (30%). Predictive ability and the degree of Correlation between factors were assessed through the Correlation-based Feature Selection (CFS) method and through the Confusion Matrix, respectively. In the training phase, all ensemble models yielded good and very good accuracies of over 84%. The spatialization of Flash-Flood Potential Index (FFPI) over the study area showed that high and very high values of flash-flood potential occur in the northern half of the region and occupy the following weights within the study area: 53.11% (FFPI Fuzzy-CART), 45.09% (Fuzzy-DLNN), 45.58% (Fuzzy-NB) and 44.85% (Fuzzy-XGBoost). The validation of the results was done through the ROC Curve method. Thus, according to Success Rate, Fuzzy-XGBoost (AUC =0.886) is the best model, while in terms of Prediction Rate, the ideal one is Fuzzy-DLNN (AUC =0.84). The novelty of this work is the application of the four ensemble models in evaluating this natural hazard.

摘要

山洪暴发对世界各个地区构成重大挑战，对生命和财产造成严重破坏。在这里，我们调查了罗马尼亚的 Izvorul Dorului 河流域，采用模糊逻辑算法与以下四种机器学习模型的组合来识别具有高山洪潜力的坡面：分类和回归树、深度学习神经网络、XGBoost、和朴素贝叶斯。使用十个山洪预测变量作为独立变量来确定山洪潜在指数。作为因变量，我们使用了具有暴雨现象的区域，分为训练 (70%) 和验证数据集 (30%)。预测能力和因素之间的相关程度分别通过基于相关性的特征选择 (CFS) 方法和混淆矩阵进行评估。在训练阶段，所有集成模型都产生了超过 84% 的良好和非常好的准确率。研究区域内山洪潜力指数 (FFPI) 的空间化表明，该地区北半部出现了高值和非常高的山洪潜力值，并在研究区域内占据以下权重：53.11% (FFPI Fuzzy -CART)、45.09% (Fuzzy-DLNN)、45.58% (Fuzzy-NB) 和 44.85% (Fuzzy-XGBoost)。结果的验证是通过 ROC 曲线方法完成的。因此，根据成功率，Fuzzy-XGBoost (AUC =0.886) 是最好的模型，而就预测率而言，最理想的模型是 Fuzzy-DLNN (AUC =0.84)。这项工作的新颖之处在于四个集合模型在评估这种自然灾害中的应用。研究区域内山洪潜力指数 (FFPI) 的空间化表明，该地区北半部出现了高值和非常高的山洪潜力值，并在研究区域内占据以下权重：53.11% (FFPI Fuzzy -CART)、45.09% (Fuzzy-DLNN)、45.58% (Fuzzy-NB) 和 44.85% (Fuzzy-XGBoost)。结果的验证是通过 ROC 曲线方法完成的。因此，根据成功率，Fuzzy-XGBoost (AUC =0.886) 是最好的模型，而就预测率而言，最理想的模型是 Fuzzy-DLNN (AUC =0.84)。这项工作的新颖之处在于四个集合模型在评估这种自然灾害中的应用。研究区域内山洪潜力指数 (FFPI) 的空间化表明，该地区北半部出现了高值和非常高的山洪潜力值，并在研究区域内占据以下权重：53.11% (FFPI Fuzzy -CART)、45.09% (Fuzzy-DLNN)、45.58% (Fuzzy-NB) 和 44.85% (Fuzzy-XGBoost)。结果的验证是通过 ROC 曲线方法完成的。因此，根据成功率，Fuzzy-XGBoost (AUC =0.886) 是最好的模型，而就预测率而言，最理想的模型是 Fuzzy-DLNN (AUC =0.84)。这项工作的新颖之处在于四个集合模型在评估这种自然灾害中的应用。