Monthly streamflow forecasting based on improved support vector machine model
Monthly streamflow forecasting based on improved support vector machine model
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DOI：
10.1016/j.eswa.2011.04.114
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发表时间：
2011-09-15
影响因子：
8.5
通讯作者：
Li, Qingqing
中科院分区：
文献类型：
--
作者：
Guo, Jun;Zhou, Jianzhong;Li, Qingqing
为了提高支持向量机（SVM）模型在月径流量预测中的性能，提出了一种具有自适应不敏感因子的改进SVM模型。与此同时摘要针对径流时间序列中噪声的影响以及传统消噪技术的不足，采用小波消噪方法对径流时间序列进行消噪处理。为了避免人工判断的主观随意性，引入相空间重构理论确定径流预测模型的结构。通过实例验证了该模型的可行性，并与人工神经网络（ANN）模型和传统SVM模型的结果进行了比较。结果表明，改进的支持向量机模型能更好地处理复杂的水文数据序列，具有更好的泛化能力和更高的预测精度。(C)2011爱思唯尔有限公司保留所有权利。
To improve the performance of the support vector machine (SVM) model in predicting monthly streamflow, an improved SVM model with adaptive insensitive factor is proposed in this paper. Meanwhile. considering the influence of noise and the disadvantages of traditional noise eliminating technologies, here the wavelet denoise method is applied to reduce or eliminate the noise in runoff time series. Furthermore, in order to avoid the subjective arbitrariness of artificial judgment, the phase-space reconstruction theory is introduced to determine the structure of the streamflow prediction model. The feasibility of the proposed model is demonstrated through a case study, and the results are compared with the results of artificial neural network (ANN) model and conventional SVM model. The results verify that the improved SVM model can process a complex hydrological data series better, and is of better generalization ability and higher prediction accuracy. (C) 2011 Elsevier Ltd. All rights reserved.