Ice Breakup Forecast in the Reach of the Yellow River: the Support Vector Machines Approach

摘要：

Accurate lead-time forecast of ice breakup is one of the key aspects for ice flood prevention and reducing losses. In this paper, Support Vector Machine (SVM) model based on the Statistical Learning Theory was employed for ice breakup prediction. In order to estimate the appropriate parameters of the SVM, Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is performed through exponential transformation. A case study was conducted in the reach of the Yellow River. Results from the proposed model showed a promising performance compared with that from artificial neural network, so the model can be considered as an alternative and practical tool for ice breakup forecast.

关键词： support vector machine artificial neural network the Yellow River multicriteria calibration

作者: Ji Liu Xiaohua Dong Yinghai Li

作者单位: College of Hydraulic and Environmental Engineering, China Three Gorges University Yichang, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 74-77

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）