...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Water Resources Management >Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques
【24h】

Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques

机译：最小二乘支持向量机结合数据预处理技术提高水流时间序列的预测精度

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.
机译：在许多水资源规划和管理活动中，高度可靠的流量预测至关重要。近年来，最小二乘支持向量机（LSSVM）方法由于能够建模复杂的非线性关系而备受关注。但是，LSSVM方法属于黑盒模型，也就是说，该方法主要基于实测数据。在本文中，我们尝试通过奇异频谱分析（SSA）和离散小波分析（DWA）从数据预处理方面提高LSSVM方法的性能。利用每月的流量数据对伊朗北部的Kharjeguil和Ponel站进行了调查。均方根误差（RMSE），平均绝对误差（MAE），相关系数（R）和效率系数（CE）统计量用作比较标准。结果表明，SSA和DWA均可显着提高预测模型的性能。但是，DWA似乎优于SSA，并且能够更准确地估计峰值流量。因此，可以建议将LSSVM方法与DWA结合使用更有前景。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号