傅新忠, 冯利华, 陈闻晨. ARIMA与ANN组合预测模型在中长期径流预报中的应用[J]. 水资源与水工程学报, 2009, 20(5): 105-109.
FU Xinzhong, FENG Lihua and CHEN Wenchen. Application of ARIMA-ANN model in the prediction of medium and long-term runoff. Journal of Water Resources and Water Engineering, 2009, 20(5): 105-109. (in Chinese)
被以下文章引用:
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标题:
径流组合预测方法的选择及其应用Selecting of Combinations Forecasting Techniques of Annual Stream-Flow and Application
作者:
粟晓玲, 孙惠子
关键字:
径流预测, 组合预测, 东大河Stream-Flow Forecast; Combining Techniques; Dongda River
期刊名称:
《Journal of Water Resources Research》, Vol.1 No.3, 2012-06-27
摘要:
为了提高径流预测的精度，选择合适的组合预测方法非常重要。通过对单项预测方法的误差序列平稳性的判断以及系统偏差校正，选择组合预测方法。应用八种组合预测技术组合了四种单项预测模型，通过相对偏差和相对均方根误差指标比较各种单项预测模型和组合预测模型的预测精度。以石羊河流域东大河的年径流预测为例，结果表明：1) 单项预测模型SVM模型和ARIMA模型效果较好；2) 通过偏差校正后的组合模型的精度普遍比未校正的组合模型预测精度高；3) WA组合方法优于SA组合方法；4) Regression和ANN组合方法能去除单项预测中的偏差，可以显著地降低组合预测的偏差。
In order to improve runoff forecast accuracy, combination forecasting method is selected by error sequence stability judgment and applying a technique with a bias correction component. Eight combinations technology were applied to combine the four single-value forecasts. The relative deviation and relative root mean square error index were used to compare the accuracy of the various single-value forecast and combined forecasts. Select the DongdaRiveras an example. The major findings include that: 1) SVM model and ARIMA model performs best among the four individual prediction models; 2) The accuracy of combining the corrected single-value forecasts is higher than combining the non-corrected single-value forecasts; 3) WA performs better than SA combination method; 4) the Regression and ANN combining methods can remove the effects of bias in the constituent forecasts and yield unbiased combining forecasts.