This paper presents a new data-driven modelling framework for forecasting probabilistic flood inundation maps for real-time applications. The proposed end-to-end (rainfall–inundation) method combines a suite of machine learning (ML) algorithms to forecast discharge and deliver probabilistic flood inundation maps with a 3 h lead time. To classify wet/dry cells, the method applies rainfall–discharge models based on random forest technique on top of classifiers based on multi-layer perceptron. The hybrid modelling framework was tested using two subsets of data created from an observed fluvial flood event in a small flood-prone town in the UK. The results showed that the model can effectively emulate the outcomes of a hydrodynamic model (Flood Modeller (FM)) with considerably high accuracy measured in terms of flood arrival time error and classification accuracy. The mean arrival time difference between the proposed model and the hydrodynamic model was 1 h 53 min. The classification accuracy was measured against a synthetic aperture radar image, producing accuracies of 88.22% and 86.58% for the proposed data-driven model and FM, respectively. The key features of the proposed modelling framework are that it is simple to implement, detects flooded cells effectively and substantially reduces computational time.

中文翻译：

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一种用于预测和可视化洪水淹没信息的机器学习方法

本文提出了一种新的数据驱动的建模框架，用于预测实时应用的概率洪水淹没图。提出的端到端（降雨-淹没）方法结合了一套机器学习（ML）算法来预测流量，并提供3小时的提前期概率洪水淹没图。为了对干/湿细胞进行分类，该方法在基于多层感知器的分类器之上应用了基于随机森林技术的降雨-流量模型。使用在英国一个容易发生洪灾的小镇观察到的河流洪水事件创建的两个数据子集，对混合建模框架进行了测试。结果表明，该模型可以有效地模拟水动力模型（Flood Modeller（FM））的结果，该模型在洪水到达时间误差和分类精度方面具有很高的准确性。提出的模型和流体动力学模型之间的平均到达时间差为1 h 53分钟。针对合成孔径雷达图像测量了分类精度，对于所提出的数据驱动模型和FM分别产生了88.22％和86.58％的准确度。所提出的建模框架的关键特征是易于实施，有效地检测淹没的单元并大大减少了计算时间。针对合成孔径雷达图像测量了分类精度，对于所提出的数据驱动模型和FM分别产生了88.22％和86.58％的准确度。所提出的建模框架的关键特征是易于实施，有效地检测淹没的单元并大大减少了计算时间。针对合成孔径雷达图像测量了分类精度，对于所提出的数据驱动模型和FM分别产生了88.22％和86.58％的准确性。所提出的建模框架的关键特征是易于实施，有效地检测淹没的单元并大大减少了计算时间。