Integrating full-process high resolution hydrodynamic simulations within early warning system (EWS) for marine flooding is hindered by the large computation time cost of such numerical models. This problem can be alleviated through the statistical analysis of pre-calculated simulation results to build a fast (low computation time cost) statistical predictive model (named metamodel). Despite the success of this approach, a direct application of such techniques for EWS is not straightforward in all settings, more particularly in environments where the stochastic character of waves has a significant effect on the induced flood, i.e., where overtopping is on a duration smaller than 500 times the offshore wave period. In such environments, the numerical simulator is not deterministic and provides statistical quantities of the flooding indicators. By focusing on the estimates of quantiles, the objective of the present study is to explore the applicability of random forest (RF) models for marine flooding prediction by providing two levels of information: (1) the quantile of interest via a quantile random forest regression model (qRF); (2) the flooding probability via a classification random forest (cRF). We use the macrotidal site of Gâvres (French Atlantic coast) as an application case for which ~ 2000 numerical simulations were performed (i.e. stochastic simulations given 100 different extreme-but-realistic offshore meteo-oceanic input conditions were repeated 20 times) to compute local and global flooding indicators (respectively the maximum water depth at the coast and the total volume of water entering the territory). Through an extensive repeated cross-validation procedure, we tune the qRF parameters leading to high coefficient of determination of ~ 90% for the quantiles at 25–50–75%, and we show that the qRF models outperform the commonly used Tobit regression model. The comparison with the numerical results on historical events shows very satisfactory prediction for events both leading to major flooding and to absence of impact. For low quantile level and minor-to-moderate flooding events, the second level provided by the cRF-derived flooding probability shows its added value by enabling the EWS user to nuance the qRF prediction and to tag some situations where the prediction remains unsure.

这种数值模型的大量计算时间成本阻碍了将全过程高分辨率流体力学模拟集成到海洋洪水预警系统中。可以通过对预先计算的模拟结果进行统计分析来缓解此问题，以建立快速的（较低的计算时间成本）统计预测模型（称为元模型）。尽管这种方法取得了成功，但并非在所有情况下都将这种技术直接应用到EWS上并非一帆风顺，尤其是在波浪的随机性对诱发洪水有显着影响的环境中，即在持续时间较小的情况下，超过海上波浪周期的500倍。在这样的环境中，数值模拟器不是确定性的，而是提供洪水指标的统计数量。通过关注分位数的估计，本研究的目的是通过提供两个级别的信息来探索随机森林（RF）模型在海洋洪水预测中的适用性：（1）通过分位数随机森林回归的目标分位数模型（qRF）; （2）通过分类随机森林（cRF）的泛洪概率。我们以加夫尔（法国大西洋海岸）的大潮站点为例，对其进行了约2000次数值模拟（即，随机模拟给出了100种不同的极端但现实的近海海洋输入条件，重复了20次）以计算局部和全球洪水指标（分别是海岸的最大水深和进入该领土的总水量）。通过广泛的重复交叉验证程序，我们对qRF参数进行了调整，导致分位数在25–50–75％之间的确定系数高达〜90％，并且我们证明qRF模型的性能优于常用的Tobit回归模型。与历史事件的数值结果的比较表明，对于导致重大洪灾和无影响的事件的预测非常令人满意。对于较低的分位数级别和轻微到中度的泛洪事件，cRF派生的泛洪概率提供的第二级通过使EWS用户能够细化qRF预测并标记某些预测仍不确定的情况来显示其附加值。与历史事件的数值结果的比较表明，对于导致重大洪水和无影响的事件的预测非常令人满意。对于较低的分位数级别和轻微到中度的泛洪事件，cRF派生的泛洪概率提供的第二级通过使EWS用户能够细化qRF预测并标记某些预测仍不确定的情况来显示其附加值。与历史事件的数值结果的比较表明，对于导致重大洪灾和无影响的事件的预测非常令人满意。对于较低的分位数级别和轻微到中度的泛洪事件，cRF派生的泛洪概率提供的第二级通过使EWS用户能够细化qRF预测并标记某些预测仍不确定的情况来显示其附加值。