基于时变权重组合与贝叶斯修正的中长期径流预报

高精度的中长期径流预报信息是水资源规划管理与水利工程经济运行的重要基础支撑。论文在组合预报与误差修正2类径流预报后处理方法串联应用的技术框架下,考虑径流的高度非平稳与非线性等特征,提出了基于时变权重组合和贝叶斯修正的中长期径流预报方法。应用该方法开展了云南龙江水库年、月入库径流预报的实例研究,结果表明时变权重组合平衡了已建立的随机森林与支持向量机模型在建模期与检验期预报性能的差异,经贝叶斯修正后的预报精度接近或优于两阶段各自的最优单一模型。根据年径流预报结果判断水文年型的正确率达到77.2%,月预报径流的确定性系数超过0.90。因此,该方法在提升中长期径流预报精度方面具有积极效果。

Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction

The mid- and long-term runoff prediction with satisfactory accuracy plays an important role as basic information in water resources planning & management and optimal operation of water conservancy projects. Combination and bias reduction are two common post-processing approaches in runoff forecast. Applying them in turn, considering the complicated non-stationary and nonlinear characteristics of runoff, a new mid- and long-term runoff prediction method by connecting time-varying weight combination and Bayesian correction is proposed. This method was used to study the annual and monthly inflow prediction of the Longjiang Reservoir in Yunnan Province. The results show that time-varying weight combination balances the performance difference of the established random forest (RF) and support vector machine (SVM) models in the modeling period and the test period. As a consequence of Bayesian correction, the prediction metrics are close to or better than the best of the predictions of the two individual stages. The proportion of correctly classified hydrological year type reaches 77.2% by employing the forecasted annual runoff, and the Nash-Sutcliffe efficiency coefficient of predicted monthly runoff series is over 0.90. Overall, the method put forward in this study has achieved positive effects in improving the forecast performance.