不同流域的自回归径流预报效果对比

Performance Comparison of Autoregressive Runoff Prediction Methods for Different River Basins

对未来月径流的可靠预报对于水力发电计划的制定和水资源调度管理具有重要的实际应用价值.由于相应预见期的气象预报不可靠以及月径流序列具有明显的非线性和随机性,导致已有模型的预报效果差异大,即使采用同一种模型,在不同流域的预报效果也显著不同.本文选取了自回归滑动平均（ARMA）模型、人工神经网络（ANN）模型和支持向量回归（SVR）模型这3种常见的径流预报模型对3个研究区域的未来一个月的径流进行预报,并用反映相对误差的平均绝对百分误差（MAPE）对预报效果进行了评估和对比分析.3个流域的预报效果对比分析表明预报效果与历史径流序列的变异系数CV以及一阶自相关系数Rlag1有关.此外,各月的径流预报的MAPE和该月历史月径流序列的CV以及Rlag1的绝对值｜Rlag1｜也显著相关,用CV和｜Rlag1｜拟合MAPE的决定系数为0.80.3个流域的流域特性分析则表明预报效果的差异本质上是由流域特性差异造成的,可以通过计算历史径流序列的CV、｜Rlag1｜判断是否适合运用数据驱动模型进行月径流预报.

Runoff prediction for the next month with historical stream flow data is concerned by many researchers due to its importance for field practices.Therefore,many data-driven models were developed for forecasting runoff for the next month.Since lack of reliable weather forecasting for the next month and randomness and nonlinearity of monthly runoff time series, the performances of those developed runoff prediction methods present significant differences.In this paper,ARMA model,ANN model and SVR model are used to forecast runoff for the next month at three river basins.The MAPE is used to evaluate the forecasting performances of these three models.The MAPE comparison results show that the performance of the SVR model is the best one among three models.The comparison of forecasting results among three river basins demonstrates that MAPE values are significant different for three river basins,which might be caused by different coefficient of variance （CV） of historical runoff time series and the correlation coefficient （Rlag1） between two adjacent months＇ runoffs.Moreover,MAPE values of three river basins have significant linear correlation with both CV and ｜Rlag1 ｜ values.When CV and ｜Rlag1 ｜ are both used to calculate MAPE, the coefficient of determination is 0. 80.Further analysis shows that the difference of river basin characteristics is the crucial reason resulting in the different MAPE values （forecasting performance） for three models at three river basins.