| 摘要: |
| 为进一步提高径流预测的精度和泛化能力，提出基于多元变量组合的回归支持向量机（SVR）集成年径流预测模型，以云南省龙潭站年均径流预测为例进行实例研究。首先，以实例1—10月月均流量作为预测因子，采用相关分析法确定预测因子与年均径流量的相关系数，按照相关系数大小顺序依次选取预测因子，构建2维输入变量~10维输入变量的9种SVR模型对实例后12年的年均径流量进行预测。最后，采用简单平均（SA）和加权平均（WA）两种集成方法对具有较高预测精度的7种SVR模型的预测结果进行综合集成。结果表明：①SVR模型的预测精度随着输入变量维数的增加明显提高。②SA-SVR和WA-SVR模型对实例后12年年均径流量预测的平均相对误差绝对值分别为1.73%和1.79%，最大相对误差绝对值分别为6.34%和6.47%，精度和泛化能力均优于各SVR模型。相对而言，由于采用多个SVR模型进行集成，SA-SVR模型预测效果略优于WA-SVR模型。 |
| 关键词: 径流预测 集成模型 回归支持向量机（SVR） 简单平均法 加权平均法 |
| DOI： |
| 分类号:P338 |
| 基金项目: |
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| A regression support vector machine integrated model based on multivariate combinations and its application |
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CUI Dong-wen
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Wenshan Water Bureau， Yunnan Province
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| Abstract: |
| In order to improve the accuracy of runoff forecast and generalization ability, a regression support vector machine (SVR) integrated annual runoff forecasting model is developed based on multivariate combinations, and the annual runoff forecasting of Longtan hydrologic station in Yunnan Province is taken as an example for the case studies. First, the average monthly discharge from January to October is taken as predictor, and correlation coefficients of the predictior and the average annual runoff are determined by a correlation analysis method. Then, the predictor is sequentially selected, according to the correlation coefficients from the maximum to the minimum, to develop nine SVR models for 2-D to 10-D input variables, and annual runoffs of next 12 years are forecasted by the models. At last, the simple average (SA) and the weighted average (WA) methods are applied to forecasting comprehensive integration for seven SVR models with high accuracy. The analysis research results show that: ①the prediction accuracy of SVR model improves significantly with the increase of imput variables dimension; ②for annual runoff predictions of next 12 years, the absolute average relative errors of SA-SVR and WA-SVR models are 1.73% and 1.79%, and the absolute maximum relative errors are 6.34% and 6.47%, which indicates that the accuracy and generalization ability of the SA-SVR and WA-SVR models are better than that of other SVR models, therefore SA-SVR model is better than WA-SVR model slightly. |
| Key words: runoff forecasting integrated model regression support vector machine(SVR) simple average method weighted average method |
