Abstract. Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e. FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas, and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through Decision Tree models trained on target FH maps, referring to a large study area (≈105 km²). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (overall accuracy: 93 %) relative to univariate ones (overall accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ≈0.7), and (c) produce encouraging results in extrapolation.

中文翻译：

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地貌描述符的机器学习混合：大型洪泛区洪水灾害评估的价值和局限性

摘要。最近的文献展示了几个基于快速计算地貌描述符的简化方法的例子，这些方法在大型地理区域内执行洪水灾害 (FH) 评估和制图。这些方法可以考虑单个索引（单变量）或同时使用一组索引（多变量）。多变量方法相对于单变量方法的潜力和准确性如何？我们能否有效地将这些方法用于外推目的，即用于建立模型的区域外的 FH 评估？我们的研究通过考虑两个单独的问题来解决这些开放性问题：(1) 绘制洪水易发地区的地图，以及 (2) 预测给定淹没情景的预期水深。我们通过在目标 FH 地图上训练的决策树模型混合了七个地貌描述符，²）。我们讨论了多变量方法相对于所选单变量模型的性能的潜力，并基于多个外推实验，其中模型在其训练区域外进行测试。我们的结果表明，多变量方法可以 (a) 相对于单变量方法（总体准确度：84 %）显着增强易受洪水影响的区域划定（总体准确度：93 %），（b）提供对预期淹没深度的准确预测（确定系数 ≈ 0.7）和（c）在外推中产生令人鼓舞的结果。