This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

中文翻译：

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深度学习神经网络和统计学习的新型集成，用于山洪敏感性映射

本研究旨在使用深度神经网络 (DNN)、层次分析法 (AHP) 和频率比 (FR) 的新混合方法评估山洪敏感性。该提议的方法选择了罗马尼亚东南部的一个集水区。在这方面，准备了一个包含 178 个洪水位置和 10 个山洪预测器的洪水地理空间数据库，并用于该提议的方法。AHP 和 FR 用于处理预测变量并将其编码为数字格式，而 DNN 是一种强大且最先进的概率机器学习方法，用于构建推理闪洪模型。模型的可靠性在接受者操作特征 (ROC) 曲线、曲线下面积 (AUC) 和一些统计测量的帮助下得到验证。结果表明，提出的两个集成模型DNN-AHP和DNN-FR能够以高于92%的准确率预测未来的山洪暴发区；因此，它们是山洪研究的新工具。