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首页> 外文期刊>Journal of Hydroinformatics >Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD
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Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD

机译：使用基于EEMD的PSO-SVM模型改进年度降雨径流预报

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摘要

Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and Nash-Sutcliffe efficiency (NSE). The PSO-SVM-EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.
机译：流域降雨径流模拟与预测是水资源管理中最重要的任务之一。在这项研究中，提出了一种自适应数据分析方法，即集成经验模型分解（EEMD），用于基于支持向量机（SVM）分解降雨-径流模型中的年降雨序列。此外，粒子群优化（PSO）用于确定SVM的自由参数。来自中国黄河大流域的研究数据用于说明该模型的性能。为了衡量模型的预测能力，将普通最小二乘（OLS）回归和典型的三层前馈人工神经网络（ANN）用作基准模型。使用均方根误差（RMSE），平均绝对相对误差（AARE），相关系数（R）和Nash-Sutcliffe效率（NSE）来测试模型的性能。 PSO-SVM-EEMD模型改善了ANN模型预测（65.99％）和OLS回归（64.40％），并降低了RMSE（67.7％）和AARE（65.38％）值。 R和NSE的预测结果分别提高了8.43％，18.89％和182.7％，164.2％。因此，本研究中提出的方法可以大大增强研究站的降雨径流预报。

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