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首页> 外文期刊>Journal of the American Water Resources Association >Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin
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Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin

机译：利用奇异价值分解的流流量预测，支持向上Rio Grande River Rio Rio Rio River Rio Railin

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摘要

The current study improves streamflow forecast lead-time by coupling climate information in a data-driven modeling framework. The spatial-temporal correlation between streamflow and oceanic-atmospheric variability represented by sea surface temperature (SST), 500-mbar geopotential height (Z(500)), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U-500) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965-2014. The April-August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1-13 months. SVD results showed the streamflow variability was better explained by SST and U-500 as compared to Z(500) and SH500. The SVM model showed satisfactory forecasting ability with best results achieved using a one-month lead to forecast the following four-month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash-Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead-time of the URGRB.
机译：目前的研究通过在数据驱动的建模框架中耦合气候信息来改善流流预测延长时间。由海表面温度（SST），500毫巴地下形高度（Z（500）），500毫巴特定湿度（SH500）和500毫巴的东西风（SH500）表示的流出和海洋大气变异性之间的空间时间相关性（U-500）太平洋和大西洋是通过奇异值分解（SVD）获得的。使用非参数方法加权SVD有效区域，并用作支持向量机（SVM）框架中的输入。选择上部Rio Grande River河流域（Urgrb），以测试拟议模型的适用性1965 - 2014年。使用前一年气候变异性预测4月8日 - 八月流出体积，创造了1-13个月的滞后关系。 SSVD结果表明，与Z（500）和SH500相比，SST和U-500的SST和U-500更好地解释了流流变化。 SVM模型显示出令人满意的预测能力，使用一个月导致实现最佳结果，以预测以下四个月期间。总体而言，SVM结果显示出优异的预测能力，平均相关系数为0.89，NASH-SUTCLIFFE效率为0.79。本研究有助于识别新的SVD重要地区并改善URGRB的流流量预测。

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