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Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition

机译：通过经验模式分解预测哥伦比亚平均每月河流量

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The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", an area of statistical analysis that studies the extraction of information from historical data to infer future trends and patterns. Our study couples the Empirical Mode Decomposition (EMD) with traditional methods, e.g. Autoregressive Model of Order 1 (AR1) and Neural Networks (NN), to predict mean monthly river discharges in Colombia, South America. The EMD allows us to decompose the historical time series of river discharges into a finite number of intrinsic mode functions (IMF) that capture the different oscillatory modes of different frequencies associated with the inherent time scales coexisting simultaneously in the signal (Huang et al. 1998, Huang and Wu 2008, Rao and Hsu, 2008). Our predictive method states that it is easier and simpler to predict each IMF at a time and then add them up together to obtain the predicted river discharge for a certain month, than predicting the full signal. This method is applied to 10 series of monthly mean river discharges in Colombia, using calibration periods of more than 25 years, and validation periods of about 12 years. Predictions are performed for time horizons spanning from 1 to 12 months. Our results show that predictions obtained through the traditional methods improve when the EMD is used as a previous step, since errors decrease by up to 13% when the AR1 model is used, and by up to 18% when using Neural Networks is combined with the EMD.
机译：哥伦比亚的水文气候学在很宽的时间范围内表现出很强的自然变异性，包括：年代际，年代际，年际，年度，年内，季节内和昼夜。多种应用部门依靠对河流排放量的定量预测来进行运营，包括水力发电，农业，人类健康，河流航行，领土规划和管理，风险防范和缓解等。基于“预测分析”（Predictive Analytics），已使用各种方法来预测月平均河流量，这是统计分析领域，用于研究从历史数据中提取信息以推断未来趋势和模式。我们的研究将经验模式分解（EMD）与传统方法相结合，例如1级（AR1）和神经网络（NN）的自回归模型，用于预测南美哥伦比亚的平均每月河水排放量。 EMD使我们能够将河流流量的历史时间序列分解为有限数量的固有模式函数（IMF），以捕获与信号中同时存在的固有时间尺度相关的不同频率的不同振荡模式（Huang等，1998）。 ，黄和吴，2008年；饶和许，2008年）。我们的预测方法指出，与预测完整信号相比，一次预测每个IMF，然后将它们加在一起以获得某个月的预测河流流量更容易，更简单。该方法适用于哥伦比亚的10个系列月平均河流量，校准期超过25年，验证期约为12年。对范围为1到12个月的时间范围执行预测。我们的结果表明，使用EMD进行下一步时，通过传统方法获得的预测会有所改善，因为使用AR1模型时，误差最多减少13％，而使用神经网络与A1模型相结合时最多减少18％。 EMD。

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  • 会议地点 Paris(FR)
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    ALEJANDRA M. CARMONA; GERMAN POVEDA;

  • 作者单位

    Department of Geosciences and Environment, Universidad Nacional de Colombia at Medellin, Carrera 80 No 65-223 -Nucleo Robledo, Medellin, Colombia;

    Department of Geosciences and Environment, Universidad Nacional de Colombia at Medellin, Carrera 80 No 65-223 -Nucleo Robledo, Medellin, Colombia;

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