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Evaluation of ocean-atmospheric indices as predictors for summer streamflow of the Yangtze River based on ROC analysis

机译:基于ROC分析的海洋大气指数预测长江夏季流量

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Antecedent anomalies of sea surface temperature and atmospheric circulation are important signals for making long-term streamflow forecasts. In this study, four groups of ocean-atmospheric indices, i.e, El Nio Southern Oscillation (ENSO), the Northern Hemisphere atmospheric circulation, the Southern Hemisphere atmospheric circulation (SAC), and the Western Pacific and Indian Ocean SST (WPI), are evaluated for forecasting summer streamflow of the Yangtze River. The gradient boosting regression tree (GBRT) is used to forecast streamflow based on each group of indices. The score based on receiver operating characteristics (ROC) curves, i.e., area under the ROC curve (AUC), is used to evaluate skills of models for identifying the high category and the low category of summer streamflow. It is found that the ENSO group and the SAC group show higher AUC values. Furthermore, both AUC values of GBRT models and individual indices show that the low flow years are easier to be identified than the high flow years. The result of this study highlights the skill from the Southern Hemisphere circulation systems for forecasting summer streamflow of the Yangtze River. Results of relative influences of predictors in GBRT models and AUC of individual indices indicate some key ocean-atmospheric indices, such as the Multivariate ENSO Index and the 500-hPa height of the east of Australia.
机译:海面温度和大气环流的前期异常是进行长期流量预报的重要信号。在这项研究中,四组海洋大气指数分别为:厄尔尼诺南方涛动(ENSO),北半球大气环流,南半球大气环流(SAC),西太平洋和印度洋海表温度(WPI)。评估长江的夏季流量。梯度增强回归树(GBRT)用于根据每组索引来预测流量。基于接收器工作特性(ROC)曲线的分数,即ROC曲线下的面积(AUC),用于评估用于识别夏季水流的高类别和低类别的模型的技能。发现ENSO组和SAC组显示出更高的AUC值。此外,GBRT模型的AUC值和各个指标均表明,与高流量年相比,低流量年更容易识别。这项研究的结果突出了南半球环流系统预测长江夏季水流的技巧。 GBRT模型中预测变量的相对影响和单个指数的AUC的结果表明了一些关键的海洋-大气指数,例如多元ENSO指数和澳大利亚东部500-hPa高度。

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