ν-支持向量机洪水预报模型研究

Flood Forecasting Model with ν-Support Vector Machine

为了提高洪水过程的非线性拟合能力和预报精度,对支持向量机洪水预报模型进行了深入研究。针对鸭绿江流域临江站的实际情况,建立了临江站洪水过程ν-SVR预报模型,采用1998-2014年间的大水年份降水资料和洪水过程资料对ν-SVR预报模型进行了率定和验证,并与线性动态系统模型、BP人工神经网络模型和ε-SVR模型进行了比较。结果表明：ν-SVR洪水预报模型比线性动态系统模型和BP人工神经网络具有较高的精度。ν-SVR洪水预报模型具有较好的非线性拟合能力和泛化能力,能很好地控制支持向量个数、降低模型的复杂程度,同时能保持良好的预报精度。

In order to improve the capacity of nonlinear fitting and accuracy of flood forecasting, the support vector machine model was studied. For the actual situation of the Linjiang Station on the Yalujiang River, the flood forecasting model for the Linjiang Station with ν-SVR forecasting model was established. The precipitation and discharge process data in the flood years from 1998 to 2014 in the watershed were used to calibrate and validatethe the model. The flood forecasting model for the Linjiang Station with ν-SVR was compared with the linear dynamic system model, BP neural network model and ε-SVR model. The results show that the flood forecasting model with ν-SVR has a high precision than the linear dynamic system model and BP neural network model. The flood forecasting model with ν-SVR has better nonlinear fitting ability and generalization ability, can control the number of support vectors and reduce the complexity of the model, while maintaining a good prediction accuracy.