Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.

提高洪水预测和制图的准确性对于减少洪水事件造成的破坏至关重要。在这项研究中，我们基于最佳第一决策树（BFT）和装袋（Bagging-BFT），装饰（Bagging-BFT）和随机子空间（RSS-BFT）集成学习技术，提出并验证了三种集成模型，用于以空间明晰的方式改进了洪水敏感性的预测。越南合安省的126次历史洪水事件与一组10个洪水影响因素有关（坡度，海拔，纵横比，曲率，河流密度，与河流的距离，流向，地质，土壤和土壤）。土地使用）以生成培训和验证数据集。这些模型通过几个性能指标进行了验证，这些指标证明了所有三个集成模型在阐明研究区域内洪水发生的潜在模式以及预测未来洪水事件发生概率方面的能力。根据接收器工作特性曲线下的面积（AUC），将获得AUC值为0.989的整体Decorate-BFT模型确定为优于RSS-BFT（AUC = 0.982）和Bagging-BFT（AUC = 0.967）型号。将模型的性能与先前文献中报道的模型进行比较，证实我们的集成模型提供了洪水敏感性的可靠估计，其结果敏感性图对于洪水预警系统以及减灾计划的制定是值得信赖的。