In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management.

在本文中，我们提出了一种新的洪水风险评估方法，该方法将深度学习算法和多准则决策分析（MCDA）相结合。洪水风险评估的框架涉及三个主要要素：危害，暴露和脆弱性。为此，越南的一个洪灾多发地区之一广南省被选为研究区域。分析了该地区过去847个洪水位置的数据，以生成模型的训练和测试数据集。在这项研究中，我们使用了一种流行的深层神经网络（DNN）算法来生成洪水敏感性图，而使用了一种流行的MCDA方法-层次分析法（AHP）来生成灾害，暴露和脆弱性。地图。我们还使用了混合模型，即BFPA和DFPA，它们是“套袋并用惩罚属性与森林进行装饰”的合奏，用于与DNNs方法进行性能比较。包括接收器工作特性（ROC）曲线在内的各种标准统计指标用于模型的性能评估和验证。结果表明，DNN和MCDA模型的集成是开发区域准确的洪水风险评估图以更好地进行洪水灾害管理的一种有前途的方法。