| 摘要: |
| 【目的】针对土石坝坝体沉降存在多变量、强耦合、强干扰的复杂问题，建立基于KPCA-RVM的土石坝沉降预测模型。【方法】利用核主元分析（KPCA）对输入向量进行降维处理，以减少因子个数，随后利用相关向量机（RVM）模型对土石坝沉降进行预测，并以平均相对误差为指标对预测精度进行评价。【结果】 实例应用表明，KPCA-RVM模型将输入向量由14个降低到7个，预测结果的平均相对误差仅为0.9%，预测效果得到明显提升。【结论】利用KPCA-RVM模型对土石坝进行沉降预测，不仅可以减少输入向量个数，而且可以提高预测精度，可在实际工程中推广应用。 |
| 关键词: 土石坝 KPCA-RVM模型 沉降预测 核主元分析 相关向量机 |
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| 基金项目:国家自然科学基金项目（51409205）；陕西省重点科技创新团队项目（2013KCT 15）；博士后自然科学基金项目（2015M572656XB）；水文水资源与水利工程科学国家重点实验室开放研究基金项目（2014491011）；西安理工大学水利水电学院青年科技创新团队项目（2016ZZKT 14） |
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| KPCA-RVM based prediction model for settlement of earth rockfill dam |
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MA Chunhui,YANG Jie,CHENG Lin,et al
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| Abstract: |
| 【Objective】A KPCA-RVM based prediction model for settlement of earth-rockfill dam was established aiming at the complex characteristics of multi variables,strong coupling and strong interference in settlement of earth-rockfill dams.【Method】The kernel principal component analysis (KPCA) was used to reduce the number of the input vectors.Then,the settlement of earth-rockfill dam was predicted using the relevant vector machine (RVM) model,and the prediction accuracy was evaluated using average relative error.【Result】The number of input vectors was reduced from 14 to 7 by KPCA-RVM model.The average relative error of prediction results was only 0.9%,indicating the prediction was significantly improved.【Conclusion】Using KPCA-RVM model to predict settlement of earth dam not only reduced the number of input vectors,but also improved the prediction accuracy.The KPCA-RVM model has great application in practical projects. |
| Key words: earth-rockfill dam KPCA-RVM model settlement prediction kernel principal component analysis relevance vector machine |
