Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.

河口上部的海平面变化传统上依赖于具有高计算成本的精细数值模拟。作为替代的高效快速解决方案，我们在这里评估了两种机器学习算法的性能：（i）基于线性和多项式回归函数的多元回归方法，以及（ii）人工神经网络，即多层感知器。这些算法被应用于 Elorn 河口上部（法国布列塔尼西部）Landerneau 市涨潮期间海平面最大值的三年观测。考虑了与潮汐和海岸浪涌对海平面的影响相关的四个输入变量：法国潮汐系数、大气压力、风速和河流流量。而这些输入变量的一部分来自具有粗糙空间分辨率的大型模型，不同的算法在这种局部环境中表现出良好的性能，因此能够捕捉半昼夜和春季小点时间尺度的海平面时间变化。迄今为止，基于对河口下游部分的观测或数值模拟的利用，预测进一步改进了对淹没事件的评估。获得的结果最终显示了风和河流流量对淹没事件的微弱影响。迄今为止，基于对河口下游部分的观测或数值模拟的利用，预测进一步改进了对淹没事件的评估。获得的结果最终显示了风和河流流量对淹没事件的微弱影响。迄今为止，基于对河口下游部分的观测或数值模拟的利用，预测进一步改进了对淹没事件的评估。获得的结果最终显示了风和河流流量对淹没事件的微弱影响。