River level forecasting is a difficult problem. Complex river dynamics lead to level series with strong time-varying serial correlation and nonlinear relations with influential factors. The current high-frequency level series present a new challenge: they are measured hourly or at finer time scales, but predictions of up to several days ahead are still needed. In this framework, prediction models must be able to provide h-step predictions for high h values. This work presents a new nonlinear model, double switching regression with ARMA errors, that addresses the features of level series. It distinguishes different regimes both in the regression and in the error terms of the model to capture time-varying correlations and nonlinear relations between response and predictors. The use of different regression and ARMA regimes will provide good h-step prediction for both low and high h values. We also propose a new estimation method that, in contrast to other switching models, does not need to define the regimes before estimating the model. This method is based on a two-step estimation and model-based recursive partitioning. The approach is applied to model the hourly levels of the Ebro River in Zaragoza (Spain), using as input an upstream location, Tudela. Using the fitted model, we obtain hourly predictions and confidence intervals up to three days ahead, with very good results. The model outperforms previous approaches, especially with high values and in cases of long-term predictions.

河流水位预报是一个难题。复杂的河流动力学导致水位序列具有很强的时变序列相关性和具有影响因素的非线性关系。当前的高频电平系列提出了新的挑战：它们每小时或以更细的时间尺度进行测量，但仍需要对未来几天进行预测。在此框架中，预测模型必须能够为高h提供h步预测价值观。这项工作提出了一个新的非线性模型，即带有ARMA错误的双重切换回归，它解决了水平序列的特征。它在模型的回归和误差方面区分了不同的机制，以捕获时变的相关性以及响应和预测变量之间的非线性关系。使用不同的回归和ARMA方案将为低h和高h提供良好的h阶跃预测价值观。我们还提出了一种新的估算方法，与其他切换模型相比，该方法无需在估算模型之前定义方案。该方法基于两步估计和基于模型的递归分区。该方法通过使用上游位置Tudela作为输入来模拟萨拉戈萨（西班牙）的埃布罗河的小时水位。使用拟合模型，我们可以提前3天获得每小时的预测和置信区间，效果非常好。该模型的性能优于以前的方法，尤其是在具有较高的价值以及长期预测的情况下。