Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere. However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods). An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans. In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations. To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified. RF and SVM also demonstrated poor performance. However, RF had a better fit than linear models, and SVM has a better fit than RF models. Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead. As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay. The predictions yielded by NARX models, for the first six months ahead, were entirely accurate. This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes. The results would serve to make short-term plans and give support to decision-makers in the management of water resources.

中文翻译：

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通过带有外生变量的非线性自回归网络改进降雨预报：以安第斯山区为例

降水是水文循环中最相关的要素，对生物圈至关重要。但是，当发生极端降雨事件时，后果可能对人类造成毁灭性的后果（干旱或洪水）。对降水的准确预测有助于决策者制定适当的缓解计划。在这项研究中，具有滞后预测因子的线性和非线性模型以及具有外生变量（NARX）网络的非线性自回归模型的实现被用来预测Labrado和Chirimachay气象站的月降雨量。为了定义合适的模型，使用了岭回归，套索，随机森林（RF），支持向量机（SVM）和NARX网络。尽管线性模型的结果“不令人满意”，但诸如Niño1 + 2，萨赫勒降雨量，确定了飓风活动，北太平洋涛动和相同的延迟降雨信号。RF和SVM的性能也很差。但是，RF具有比线性模型更好的拟合度，而SVM具有比RF模型更好的拟合度。取而代之的是，使用多种架构对NARX模型进行了训练，以便为未来12个月的最佳预测确定最佳架构。总体而言，NARX模型对Labrado的显示为“良好”，对Chirimachay的显示为“令人满意”。NARX模型对未来六个月的预测是完全准确的。这项研究强调了NARX网络在预测混沌和非线性信号（如服从复杂过程的地区的降雨）中的优势。