基于EEMD-PSO-GRNN模型的元江中上游径流预测

径流序列的非线性和非平稳特性使得高精度的径流预报存在困难。本文组合EEMD和GRNN模型形成EEMD-GRNN耦合模型,预测时通过将径流序列分解为确定成分与随机成分并通过GRNN模型分别进行预测,预测值的加和则构成径流最终预测结果。EEMD-GRNN耦合模型应用到元江中上游,并与其他模型进行比较,结果表明：EEMD-GRNN耦合模型具有更高的预测精度,对径流的总体趋势预测有良好的效果,但在随机性的模拟上有待进一步完善。EEMD-GRNN耦合模型优于BP、GRNN、EEMD-BP模型,能有效提升径流预测的精度,可为流域的水资源优化调度等提供决策支持。

Runoff prediction in the middle and upper reaches of Yuanjiang River Based on EEMD-GRNN model

The non-linear and non-stationary characteristics of runoff series make it difficult to forecast runoff with high accuracy. In this paper, the EEMD-GRNN coupled model is formed by combining the EEMD and GRNN models, and the runoff series is decomposed into deterministic and stochastic components and predicted separately by the GRNN model. The sum of the predicted values constitutes the final predicted value of runoff. The EEMD-GRNN coupling model is applied to the middle and upper reaches of Yuanjiang River and compared with the prediction effects of other models. The results show that the EEMD-GRNN coupled model has a higher prediction accuracy and is effective in predicting the overall trend of runoff, but the stochasticity simulation needs to be further improved. The EEMD-GRNN coupled model performances better than BP, GRNN and EEMD-BP models, and can effectively improve the accuracy of runoff prediction and provide decision support for water resources optimization in the basin.