Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Nino Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

中文翻译：

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使用人工神经网络方法对哥伦比亚西南部的月降雨异常进行预测

提高降雨预报的准确性与适当的水资源规划和管理相关。该研究项目评估了三种人工神经网络 (ANN) 方法的组合在预测哥伦比亚西南部每月降雨量异常方面的性能。为此，我们应用非线性主成分分析 (NLPCA) 方法来获得主要模式，使用外生变量的神经网络自回归移动平均 (NNARMAX) 作为模型，并使用逆 NLPCA 方法来重建月降雨量异常分别在哥伦比亚西南部的安第斯地区 (AR) 和太平洋地区 (PR) 进行预测。对于模型，我们使用与厄尔尼诺南方涛动 (ENSO) 现象相关的八个大型气候指数的月降雨滞后值作为外生变量。它们与使用 NLPCA 获得的 AR 和 PR 降雨变化的主要模式互相关。随后，两个 NNARMAX 模型都从 1983 年到 2014 年进行了训练，并进行了两年的测试（2015 年到 2016 年）。最后，来自 NNARMAX 模型的重构输出用作逆 NLPCA 方法的输入。ANN 方法的性能使用三种不同的性能指标来衡量：均方根误差 (RMSE)、平均绝对误差 (MAE) 和皮尔逊相关性 (r)。结果表明对 AR 和 PR 具有合适的预测性能，